Smartphone use, health-promoting behaviours and self-rated health among middle-aged and elderly people in rural areas

doi:10.21203/rs.3.rs-4953641/v1

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Research Article

Smartphone use, health-promoting behaviours and self-rated health among middle-aged and elderly people in rural areas

https://doi.org/10.21203/rs.3.rs-4953641/v1

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Objective

Accounting for the convergence of digitalization and ageing, our study focused on middle-aged and elderly people in rural areas. Moreover, the potential mechanisms by which smartphone use affects the self-rated health and heterogeneity among different groups were explored, and the mediating effects of health-promoting behaviours were explored, with the aim of providing relevant insights and recommendations for improving the health of rural middle-aged and elderly people and actively promoting healthy ageing in rural areas.

Methods

On the basis of data from the 2020 China Rural Revitalization Survey, we established a multiple linear regression model to assess the direct effects of smartphone use (including whether to use, difficulty of use, and duration of use) on the self-rated health of rural middle-aged and elderly people and examined the heterogeneity among the various groups in terms of sex, age, and education level. In addition, the three-step regression and bootstrap test methods were used to analyse the mediating effect of health-promoting behaviours on the relationship between smartphone use and self-rated health.

Results

Smartphone use among rural middle-aged and elderly people significantly and positively affected their health-promoting behaviours and self-rated health, and the findings were robust. The positive effects of smartphone use on self-rated health were heterogeneous among rural middle-aged and older adults of different ages, sexes, and education levels. Health-promoting behaviours exerted significant mediating effects, accounting for 91.91%, 95.27% and 90.91% of the total effects, respectively.

Conclusion

Smartphone use among rural middle-aged and elderly people notably affected the improvement in their self-rated health, and this positive effect differed according to sex, age and education level. The indirect path of encouraging rural middle-aged and elderly people to use smartphones, reducing the difficulty of smartphone use, and prolonging the duration of smartphone use for enhancing health-promoting behaviours could effectively improve their self-rated health.

Smartphone use

Health-promoting behaviours

Self-rated health

Rural middle-aged and elderly people

Healthy ageing

At present, demographic transformation in China is accelerating, with a very large elderly population that accounts for a very fast increasing proportion of the total population. It is predicted that in approximately 2035, China will officially become a severely ageing society and will remain so for a long period. We should avoid the series of "grey rhinoceros" risks, such as the risk of disease spread, the uneven distribution of old-age risks, and the risk of insufficient pension security for residents[1, 2]. Healthy ageing is considered a means to address population ageing and is associated with the lowest cost and the greatest benefits[3].

The urban‒rural inversion phenomenon is a typical feature of the population ageing process in China. There are notable differences in the degree of rural ageing and the self-rated health status of elderly people between rural and urban areas, and this phenomenon is exacerbated over time[4, 5]. Moreover, the development of the digital economy in China has rapidly progressed, and digital technology has penetrated all aspects of human production and life at an unprecedented speed. However, owing to contact barriers, knowledge ageing, and physical reasons, middle-aged and elderly people are at a disadvantage in terms of network access, digital equipment and the use of digital technologies. They are even aptly referred to as “digital immigrants” and “digital refugees”[6]. According to the 53rd Statistical Report on Internet Development in China released by the China Internet Network Information Centre, as of December 2023, the number of non-internet users in China reached 317 million individuals, with non-internet users in rural areas accounting for 51.8% of the total number. The elderly group accounted for 39.8% of the total sample, and the terms “rural” and “elderly” are still the two key labels for non-internet users in China[7]. Therefore, at the convergence of the digital wave and the ageing process, accounting for digital inclusion and the health status of middle-aged and elderly people in rural areas is highly practical.

As the internet continues to penetrate middle-aged and elderly populations, more middle-aged and elderly people have begun to access and embrace digital life. Most existing studies emphasize the health dividends resulting from the use of digital technology for middle-aged and elderly people. For example, the use of the internet, smartphones, and mobile applications such as WeChat positively affects the promotion of the healthy lifestyle, the increase in proactive health consciousness, and the improvement in the health levels of middle-aged and elderly people[8–12], and relevant conclusions have also been obtained for middle-aged and elderly people in rural areas[13–16]. In addition, scholars have noted that moderate internet use positively affects healthy ageing and the subjective well-being of middle-aged and elderly people[17], but excessive and high-frequency use of smartphones and the internet is detrimental to the physical and mental health of elderly people[13, 18], as it will make them psychologically dependent and lead to problems such as weakened social network, dysfunctional intergenerational relationships, and reduced sleep quality[13, 19, 20]. Furthermore, other scholars have examined the internal mechanisms of digital technology health empowerment and noted that the use of the internet by elderly people can increase the frequency of exercise and learning, improve social participation and social trust and facilitate their access to formal and informal social support structures. Therefore, their physical and mental health conditions can be improved[10, 21–24]. In addition, Zhu et al. reported that internet use can improve the physical and mental health of rural residents by promoting their leisure and entertainment, social networking, and knowledge acquisition activities[14], but some studies have indicated that internet use significantly inhibits the frequency of physical activities among rural residents and that the inhibitory effect is greater for rural residents aged 40 and over than for other age groups[25]. However, through a literature review, most recent studies are based on comprehensive national survey data and focus on middle-aged and elderly people as a whole, although this research has involved evaluating the heterogeneity in the dichotomous structure between rural and urban areas. However, in general, mechanistic studies on the relationship between digital inclusion and the health of rural middle-aged and elderly people remain very rare, especially mechanistic studies involving the improvement in active health ability levels. National Health Commission of PRC issued the 14th Five-Year Plan for Healthy Aging, which clearly states that elderly individuals should be guided to adopt the goal of “maintaining body functions and the ability to live independently”, establish the awareness that “they are the first person responsible for health”, and promote the development of the health of elderly people. A healthy lifestyle is recommended to improve the health of elderly people[26].

In summary, this study focused on middle-aged and elderly people in rural areas, and national survey data for rural areas were used to empirically analyse the effects of smartphone use (including whether to use, difficulty of use and duration of use) on the self-rated health of middle-aged and elderly people as well as its heterogeneous performance in different individual characteristics. Therefore, we determined whether health-promoting behaviour exerts a mediating effect, and we examined the corresponding effects on improving the health status of middle-aged and elderly people and actively promoting healthy ageing in rural areas. This study aimed to narrow the health gap between urban and rural areas and provide relevant insights and suggestions for a methodical response to population ageing in China.

Data sources

In this paper, China Rural Revitalization Survey data released by the Institute of Rural Development of the Chinese Academy of Social Sciences were used. The data were derived from the first phase (2020) of the China Rural Revitalization Survey (CRRS). The survey was conducted between August and September 2020. First, provinces were selected via the random sampling method. Second, sample counties, townships (towns) and villages were selected via the equidistant random sampling method on the basis of the per capita gross domestic (GDP). Finally, samples were selected from Guangdong, Zhejiang, Shandong, Anhui, and Henan Provinces, including survey data covering 50 counties (cities) and 156 townships (towns) across China, and more than 3800 farmer household questionnaires were obtained. In this study, on the basis of the research population, samples of middle-aged and elderly people aged 45 years and over were screened, and the individual characteristics of rural households, information use status, health status, and health behaviour were assessed on the basis of the research findings. Outliers and missing values were removed. Notably, middle-aged and elderly people in rural areas were effectively sampled, which yielded database 1 in this paper. Furthermore, the group of individuals who did not use smartphones was excluded to investigate the issues of the difficulty and duration of smartphone use, and a total of 2221 effective samples were obtained, which yielded database 2.

Variable selection

Dependent variable

The self-rated health status was adopted as the dependent variable. Specifically, questions, such as “how is your health status compared with that of your peers? (20 − 5)”, were used to reflect the health status of middle-aged and elderly people in rural areas. This question was scored positively via the Likert five-point scoring method, where a value of 1 indicated “very poor”, and a value of 5 indicated “very good”. Afterwards, the options of the self-rated health part were combined to generate a new three-category dependent variable (i.e., “poor,” “fair” and “good”) for stability testing.

Independent variable

The use of smartphones was employed as the independent variable. This variable was analysed via questions related to the following three issues: (1) whether to use a smartphone (8 − 3: “do you use a 4G/5G mobile phone?”), where a value of 1 indicated that a 4G/5G mobile phone was used, and a value of 0 indicated the opposite; (2) difficulty of smartphone use (8 − 4: “do you have difficulty using the functions of 4G/5G mobile phones?”), which was treated as an ordinal variable, with values of 1–3 assigned, where a value of 1 indicated no difficulty, a value of 2 indicated some difficulty, and a value of 3 indicated great difficulty (i.e., phones were only used to make and receive calls). Notably, the latter two categories (2 and 3) were combined to indicate that it was “difficult to use smartphones”, which was assigned a value of 1, and the remaining cases were assigned a value of 0, thus generating a new binary variable of the difficulty of smartphone use for robustness testing. (3) The duration of smartphone use (8 − 5: “what is your average duration of 4G/5G mobile phone use per day?”) was treated as a continuous numerical variable, namely, the number of hours indicated by the respondent. Afterwards, the mean duration of smartphone use was calculated. A value higher than the mean was defined as a high usage duration, which was assigned a label of 1. A value lower than the mean was defined as a low usage duration, which was assigned a label of 0. Furthermore, as stability check was performed.

Mediating variable

Health-promoting behaviour was considered a mediating variable. Drawing on the study of Cao et al[27], this study focused on investigating the health-promoting behaviours of middle-aged and elderly people in rural areas from the three aspects of exercise, nutrition and health responsibility by asking the following questions: ① 20 − 10: “did you do more than 30 minutes of fitness or exercise activity in the last week”② 20 − 13: “do you consciously control your sugar intake?”; ③ 20 − 14: “do you consciously control your salt intake?”; ④ 20 − 15: “do you consciously control your edible oil intake?”; ⑤ 20 − 16: “do you consume health products in your daily life?”; ⑥ 13 − 8: “do you participate in pension insurance?”; ⑦ 20 − 11: “did you have a medical examination in the past year?”; ⑧ 20 − 9: “do you currently purchase health products or have commercial medical insurance?”; and ⑨ 20 − 12: “did you consciously study health or health preservation knowledge?”. The answers to the above questions were all regarded as binary variables, where a value of 0 indicated no and where a value of 1 indicated yes. The total score of the health-promoting behaviours of middle-aged and elderly people was calculated via summation. A higher score indicated greater individual health-promoting behaviour.

Control variables

In addition, in accordance with a previous study[28], to reduce the effect of systematic bias, six variables, namely, sex, age, education level, body mass index (BMI), ordinary villager status, and chronic diseases, were included as control variables. Notably, sex was treated as a binary variable, with 1 indicating male and 2 indicating female; ordinary villager status was treated as a dichotomous variable, with 1 indicating the status of an ordinary villager and 2 indicating other positions in the village (such as village party secretary, village director, village committee member, and group leader); the occurrence of chronic diseases was treated as a binary variable, with 1 indicating the occurrence of chronic diseases and 0 indicating the absence thereof; age was treated as a continuous numerical variable; and education level was treated as an ordinal variable, thereby assigning values of 1–3, where 1 indicated a primary school education and below, 2 indicated a junior high school education, and 3 indicated a high school education and above. The BMI was calculated by dividing the weight (in kilograms) by the square of the height (in metres). Notably, 18.5 ≤ BMI < 24 indicated a normal weight, which was assigned a value of 1. Values outside this range indicated an abnormal weight, which was assigned a value of 0.

Data analysis

SPSS 26.0 software was used for statistical analysis. First, a multivariate linear regression model was established to assess the effect of smartphone use on the self-rated health of middle-aged and elderly people in rural areas, and a stability test was performed of the baseline regression results. Previous studies have shown that smartphone use imposes differential effects on the health status of different groups of people[29, 30]. Therefore, in this study, heterogeneity analysis was employed to determine whether there are sex, age and education level differences in the effect of smartphone use on self-rated health among middle-aged and elderly people in rural areas. Finally, a three-step regression method was adopted to examine the mediating effect of health-promoting behaviours on the relationship between smartphone use and self-rated health among middle-aged and elderly people, and the bootstrap test was used to analyse whether health-promoting behaviours imposed a mediating effect on the relationship between these two aspects, and to compare the relationship between the indirect effect and the direct effect and its magnitude.

Descriptive statistics

As indicated in Table 1, the mean age of the 2876 middle-aged and elderly people included in this study was 58.82 ± 8.54 years, with 78.79% males and 21.21% females. In terms of education level, a primary school education and below and a junior high school education accounted for the majority, namely, 41.97% and 44.30%, respectively, whereas a high school education and above accounted for only 13.56% of the total results. According to the survey results, 42.94% of middle-aged and elderly people indicated that they suffered chronic diseases, and the BMI value of 50.42% of middle-aged and elderly people reached the normal level. The self-rated health of middle-aged and elderly people in rural areas was relatively good. A total of 37.97% of middle-aged and elderly people rated their health as “good,” while 16.55% and 29.66% of the considered individuals rated their health as “very good” and “fair,” respectively.

Among the 2876 middle-aged and elderly people included in the analysis, 2221 individuals used smartphones, accounting for 77.23% of the total number, indicating that the considered middle-aged and elderly people exhibited a high smartphone usage rate. Among the 2221 people who used smartphones, 43.13% noted that they experienced no difficulty using smartphones, while 42.14% noted that they experienced some difficulty using smartphones. Moreover, the average duration of smartphone use was 2.44 ± 1.86 hours. The average score of the health-promoting behaviour parameter was 4.66 ± 2.14 points, which is slightly higher than the theoretical mean score of 4.5 points, indicating that the health-promoting behaviours of rural middle-aged and elderly people were moderate.

Table 1

Descriptive statistics of the samples
Variable	Database 1 (N = 2876)		Database 2 (N = 2221)
Variable	Frequency/mean ^a	Proportion/variance ^b	Frequency/mean ^a	Proportion/variance ^b
Self-rated health
Very poor	82	2.85%	63	2.84%
Poor	373	12.97%	255	11.48%
Average	853	29.66%	649	29.22%
Good	1092	37.97%	874	39.35%
Very good	476	16.55%	380	17.11%
Smartphone use (yes = 1)	2221	77.23%
Difficulty of smartphone use
No difficulty			958	43.13%
Some difficulty			936	42.14%
It is very difficult, and the phone is only used for making and receiving calls			327	14.72%
Smartphone use duration			2.44^a	1.86^b
Health-promoting behaviour	4.66^a	2.14^b	4.75^a	2.18^b
Whether they performed more than 30 minutes of fitness or exercise activities in the past week (yes = 1)	1679	58.40%	1360	38.77%
Do they consciously control their sugar intake (yes = 1)	1740	60.50%	1367	61.55%
Do they consciously control their salt intake (yes = 1)	1797	62.48%	1418	63.85%
Do they consciously control their edible oil intake (yes = 1)	1712	59.53%	1353	60.92%
Do they consume health products in their daily life (yes = 1)	403	14.01%	297	13.37%
Whether they participate in pension insurance (yes = 1)	2451	85.22%	1910	86.00%
Whether they had a medical examination in the last year (yes = 1)	1934	67.25%	1442	64.93%
Do they have commercial medical insurance (yes = 1)	401	13.94%	355	15.98%
Do they consciously study health or regimen knowledge (yes = 1)	1275	44.33%	1056	47.55%
Sex (male = 1)	2266	78.79%	1733	78.03%
Age	58.82^a	8.54^b	56.32^a	7.16^b
Education level
Primary school education and below	1207	41.97%	765	34.44%
Junior high school education	1274	44.30%	1093	49.21%
High school education and above	390	13.56%	363	16.34%
Villager (yes = 1)	2293	79.73%	1686	75.91%
BMI (normal = 1)	1450	50.42%	1067	48.04%
Chronic diseases (yes = 1)	1235	42.94%	909	40.93%
Note: ^a denotes the mean value; ^b denotes the variance.

Baseline regression results

As indicated in Table 2, Models 1, 3 and 5 captured only the core independent variables, whereas Models 2, 4 and 6 are estimates of the complete regression model with control variables added. The results revealed that smartphone use and the duration of such use consistently and significantly positively promoted the self-rated health of rural middle-aged and elderly people, whereas the difficulty of smartphone use significantly and negatively affected the self-rated health of rural middle-aged and elderly people. In addition, the five control variables of age, education level, ordinary villager status, BMI and chronic diseases were significant in the three models. Age imposed a positive U-shaped effect on the self-rated health of rural middle-aged and elderly people. The self-rated health of rural middle-aged and elderly people with higher education levels, a nonordinary villager status, a normal BMI and no chronic diseases was better.

Robustness test

Table 3 lists the results of the stability test after replacing the dependent and independent variables. The results indicated that, first, after the dependent variables were replaced, the use of smartphones, the difficulty and duration of smartphone use still significantly affected the self-rated health of middle-aged and elderly people in rural areas. Second, after the independent variables were replaced, the difficulty and duration of smartphone use still significantly affected the self-rated health of middle-aged and elderly people in rural areas, validating the estimation results of the baseline model.

Heterogeneity analysis

Age heterogeneity analysis

With reference to previous studies, the samples were divided into two groups: middle-aged individuals that are 46–59 years old and elderly individuals that are 60 years and older[31]. According to Table 4, smartphone use positively affected the self-rated health of the middle-aged population, but the effect was not significant. Notably, smartphone use imposed a significant positive effect on the self-rated health of the elderly population. Moreover, the difficulty of using smartphones imposed a significant negative effect both on the self-rated health of middle-aged people and elderly people, indicating that reducing the difficulty of smartphone use could improve the self-rated health of middle-aged and elderly people in rural areas. In addition, a longer duration of smartphone use significantly improved the self-rated health in the middle-aged population, but the improvement was not significant in the elderly population.

Sex heterogeneity analysis

The samples were divided into two groups according to sex, namely, male and female, and a heterogeneity test was performed. According to Table 5, smartphone use imposed a significant positive effect on the self-rated health of males, but the improvement effect on the self-rated health of females was not significant. Moreover, the difficulty of smartphone use significantly restricted the self-rated health of both males and females, whereas the duration of smartphone use significantly improved both the self-rated health of both groups.

Education level heterogeneity analysis

The education level was divided into three groups for further heterogeneity testing: elementary school education and below, junior high school education, and high school education and above. According to Table 6, smartphone use imposed a positive and significant effect on the self-rated health of rural middle-aged and elderly people with a junior high school education and a high school education and above, but the improvement effect on the population with a primary school education and below was not significant. Moreover, the difficulty of smartphone use significantly inhibited the self-rated health of rural middle-aged and elderly people with primary school educations and below and junior high school educations, whereas the duration of smartphone use significantly improved the health of only rural middle-aged and elderly people with junior high school educations.

Table 2

Baseline regression results
Variable	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Smartphone use	0.176*** (0.045)	0.135** (0.051)
Difficulty of smartphone use			-0.242*** (0.029)	-0.169*** (0.030)
Smartphone use duration					0.130** (0.041)	0.032** (0.011)
Sex		-0.068 (0.043)		-0.076 (0.048)		-0.084* (0.048)
Age		-0.066** (0.027)		-0.069* (0.038)		-0.085** (0.038)
Age squared		0.001** (0.000)		0.001** (0.000)		0.001** (0.000)
Education level		0.074** (0.027)		0.069** (0.030)		0.095** (0.029)
Ordinary villager		0.200*** (0.045)		0.177*** (0.046)		0.181*** (0.047)
BMI		0.138*** (0.035)		0.164*** (0.039)		0.173*** (0.039)
Chronic diseases		-0.748*** (0.036)		-0.731*** (0.040)		-0.758*** (0.040)
N	2876	2876	2221	2221	2221	2221
F value	15.639	64.378	67.052	56.649	10.073	54.082
Adjusted R²	0.005	0.150	0.029	0.168	0.004	0.161

Note: *, ** and *** denote significance at the 10%, 5% and 1% levels, respectively, and the standard errors are listed in parentheses.

Table 3

Results of robustness test
Variable	Self-rated health (three categories)			Self-rated health (five categories)
Smartphone use	0.123** (0.038)
Difficulty of smartphone use		-0.123*** (0.022)
Smartphone use duration			0.020** (0.008)
Difficulty of smartphone use (binary classification)				0.262*** (0.042)
Smartphone use duration (binary classification)					0.088** (0.040)
Control variables	Control	Control	Control	Control	Control
N	2876	2221	2221	2221	2221
F value	62.694	53.316	50.400	57.747	53.475
Adjusted R²	0.147	0.159	0.151	0.171	0.159
*Note: , and * denote significance at the 10%, 5% and 1% levels, respectively, and the standard errors are provided in parentheses.**

Table 4

Results of age heterogeneity analysis
Variable	Middle-aged people	Elderly people	Middle-aged people	Elderly people	Middle-aged people	Elderly people
Smartphone use	0.097 (0.097)	0.113* (0.058)
Difficulty of smartphone use			-0.163*** (0.036)	-0.160** (0.050)
Smartphone use duration					0.031** (0.012)	0.034 (0.022)
Control variables	Control	Control	Control	Control	Control	Control
N	1672	1204	1572	649	1572	649
F value	53.505	31.367	55.862	19.429	53.059	19.014
Adjusted R²	0.159	0.132	0.174	0.148	0.166	0.143
*Note: , and * denote significance at the 10%, 5% and 1% levels, respectively, and the standard errors are provided in parentheses.**

Table 5

Results of sex heterogeneity analysis
Variable	Male	Female	Male	Female	Male	Female
Smartphone use	0.164** (0.056)	0.013 (0.123)
Difficulty of smartphone use			-0.133*** (0.033)	-0.292*** (0.069)
Smartphone use duration					0.022* (0.012)	0.068** (0.025)
Control variables	Control	Control	Control	Control	Control	Control
N	2266	610	1733	488	1733	488
F value	56.601	16.425	45.901	18.242	44.830	16.358
Adjusted R²	0.147	0.151	0.54	0.199	0.151	0.181
*Note: , and * denote significance at the 10%, 5% and 1% levels, respectively, and the standard errors are listed in parentheses.**

Table 6

Results of education level heterogeneity analysis
Variable	Primary school education and below	Junior high school education	High school education and above	Primary school education and below	Junior high school education	High school education and above	Primary school education and below	Junior high school education	High school education and above
Smartphone use	0.060 (0.070)	0.189** (0.085)	0.692*** (0.191)
Difficulty of smartphone use				-0.113** (0.051)	-0.230*** (0.043)	-0.119 (0.073)
Smartphone use duration							0.024 (0.021)	0.038** (0.015)	0.030 (0.022)
Control variables	Control	Control	Control	Control	Control	Control	Control	Control	Control
N	1207	1274	390	763	1091	363	763	1091	363
F value	30.969	30.045	11.231	24.542	29.964	7.709	24.228	26.578	7.945
Adjusted R²	0.148	0.138	0.155	0.178	0.157	0.116	0.176	0.141	0.118
*Note: , and * indicate significance at the 10%, 5% and 1% levels, respectively; standard errors are provided in parentheses.**

Mechanism analysis

We draw on the basic idea of Baron[32] on mediating effect analysis, the three-step regression method was used to analyse the mechanism of the effect of smartphone use on the self-rated health of rural middle-aged and elderly people. Regression was performed using health-promoting behaviour and self-rated health as the dependent variables and smartphone use, difficulty of use, and duration of use as the independent variables. According to Table 7, smartphone use among rural middle-aged and elderly people significantly and positively affected their health-promoting behaviour (β = 0.390, P < 0.05) and self-rated health (β = 0.135, P < 0.05); the difficulty of using smartphones among rural middle-aged and elderly people significantly and negatively affected their health-promoting behaviour (β = -0.377, P < 0.001) and self-rated health (β = -0.169, P < 0.001); and the duration of smartphone use among rural middle-aged and elderly people significantly and positively affected their health-promoting behaviour (β = 0.089, P < 0.001) and self-rated health (β = 0.032, P < 0.05). The study results revealed that rural middle-aged and elderly people who use smartphones exhibit less difficulty using smartphones and longer use times are associated with greater health-promoting behaviours and higher self-rated health levels. Moreover, self-rated health was used as a dependent variable to regress smartphone use, difficulty of use, and duration of use and health-promoting behaviour in the same model, and it was determined that health-promoting behaviour imposed a significant positive effect on the self-rated health (β = 0.025, P < 0.05; β = 0.021, P < 0.05; β = 0.024, P < 0.05). In summary, the mediating effect test of health-promoting behaviour was passed, namely, the health-promoting behaviour play a mediating role in the relationship between smartphone use, difficulty of use, and duration of use affecting self-rated health among rural middle-aged and elderly people.

A bootstrap test was conducted. The number of repeated samples was set to 5000, and the confidence interval was set to 95% to verify the mediating effect of health-promoting behaviour. According to Table 8, first, the direct effect of smartphone use on the self-rated health of middle-aged and elderly people in rural areas was 0.011, while the indirect effect of health-promoting behaviour was 0.125. Notably, the results were statistically significant, with the indirect effect accounting for 91.91% of the overall variance. Second, the direct effect of the difficulty of smartphone use on the self-rated health of middle-aged and elderly people in rural areas was − 0.008, while the indirect effect of health-promoting behaviour was − 0.161. The results were statistically significant, and the indirect effect accounted for 95.27% of the total variance. Finally, the direct effect of smartphone use time on the self-rated health of middle-aged and elderly people in rural areas was 0.003, while the indirect effect of health-promoting behaviour was 0.030. The obtained results were statistically significant, and the indirect effect accounted for 90.9% of the total variance.

Table 7

Estimates of the mediating effect of health-promoting behaviour
Variable	Health-promoting behaviour			Self-rated health
Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Smartphone use	0.390** (0.113)			0.135** (0.051)			0.125* (0.051)
Difficulty of smartphone use		-0.377*** (0.068)			-0.169*** (0.030)			-0.161*** (0.030)
Smartphone use duration			0.089*** (0.025)			0.032** (0.011)			0.030** (0.011)
Health-promoting behaviour							0.025** (0.008)	0.021** (0.009)	0.024** (0.009)
Control variables	Control	Control	Control	Control	Control	Control	Control	Control	Control
N	2876	2221	2221	2876	2221	2221	2876	2221	2221
F value	28.519	25.654	23.499	64.378	56.649	54.082	58.399	51.009	48.973
Adjusted R²	0.071	0.082	0.075	0.150	0.168	0.161	0.153	0.169	0.163
*Note: , and * denote significance at the 10%, 5% and 1% levels, respectively.**

Table 8

Results of bootstrap test
Independent variable	Effect	Path relationship	β	SE	Lower limit	Upper limit	Effect size
Smartphone use	Direct effect	Smartphone use → self-rated health	0.011**	0.004	0.004	0.021	8.09%
	Indirect effect	Smartphone use → health-promoting behaviour → self-rated health	0.125**	0.052	0.026	0.227	91.91%
	Total effect		0.136**	0.051	0.038	0.237	100.00%
Difficulty of smartphone use	Direct effect	Difficulty of smartphone use → self-rated health	-0.008**	0.004	-0.017	-0.002	4.73%
	Indirect effect	Difficulty of smartphone use → health-promoting behaviour → self-rated health	-0.161***	0.030	-0.221	-0.102	95.27%
	Total effect		-0.169***	0.030	-0.230	-0.111	100.00%
Smartphone use duration	Direct effect	Duration of smartphone use → self-rated health	0.003**	0.001	0.001	0.006	9.09%
	Indirect effect	Duration of smartphone use → health-promoting behaviour → self-rated health	0.030**	0.011	0.010	0.050	90.91%
	Total effect		0.033**	0.011	0.013	0.053	100%

Note: , and denote significance at the 10%, 5% and 1% levels, respectively

Smartphone use among rural middle-aged and elderly people directly reinforces health-promoting behaviour

In this study, it was revealed that smartphone use among rural middle-aged and elderly people could directly reinforce health-promoting behaviour. Health-promoting behaviour requires individuals to exert subjective initiative, in which knowledge plays an important role[33]. With the rapid development of digital technology, many types of health information are presented in electronic form, and the internet has become an important source of health information for people[34]. Numerous previous studies have shown that smartphones provide channels for middle-aged and elderly people to obtain information and education, and that middle-aged and elderly people can use smartphones to obtain online health information and learn ways and methods to search for and acquire health knowledge, which will be conducive to the accumulation of their health knowledge [11, 35, 36], and that knowledge is one of the important prerequisites for behaviour change, which in turn can guide middle-aged and elderly people to adopt healthier behaviours[37]. Xavier et al analysed data from 5,900 older people in the English Longitudinal Study of Ageing and found that internet use was associated with weekly moderate to vigorous exercise, more frequent daily consumption of fruit and vegetables and less smoking[38]. In addition, relevant studies on rural groups have indicated that rural residents who often use smartphones are more inclined to adopt digital tools for health promotion, consider diverse sources of digital health information and explore various digital health-promoting behaviours, such as searching for health information and health status management and tracking improving, to enhance their health levels[39].

Smartphone use among rural middle-aged and elderly people directly affects their self-rated health

The relevant results showed that smartphone use among rural middle-aged and elderly people could directly improve their self-rated health. Similarly, previous studies have shown that smartphone use can effectively sustain the social connections of middle-aged and elderly people with other groups, expand their social networks, and reduce social alienation[40–42], thereby positively affecting their health[43–45]. In addition, Liu and Guo reported that the use of mobile internet applications, including WeChat, WeChat Moments and mobile payments, can effectively promote physical and mental health[46]. Studies have also demonstrated that middle-aged and elderly people can use smartphones to search for and browse a variety of online health information, obtain medical information, communicate with health care professionals, and perform other health-related tasks, thus positively affecting their health[12, 46–48]. For the rural population, numerous empirical studies have revealed that internet use can significantly improve the self-rated health level of middle-aged and elderly people in rural areas[49, 50] and that the health of Chinese elderly people is affected mainly through social support[51, 52]. Moreover, some studies have indicated that watching short videos can positively influence the acquisition of online health information by rural elderly people. As mentioned previously, online health information is an important prefactor influencing the health status[53].

The effect of smartphone use on the self-rated health varies among rural middle-aged and elderly people of different sexes, ages and education levels

Through heterogeneity analysis, we found that sex, age and education level differences exist in the effects of smartphone use on the self-rated health of middle-aged and elderly people in rural areas, which is similar to the conclusions of existing studies[8, 24, 29, 50]. In terms of gender differences, the effect of smartphone use on the self-rated health of rural elderly males was greater than that on rural elderly females. There is significant gender inequality in rural education opportunities, namely, rural women are at a disadvantage in obtaining educational opportunities, whereas rural men are more likely to obtain better educational opportunities and reach higher educational levels[54]. In addition, under the influence of traditional cultural thinking, the traditional family division of labour model, in which “males dominate the workplace and females dominate the home,” is still common[55]. Given the need to maintain social networks, middle-aged and elderly men use smartphones more frequently. Therefore, men face fewer barriers to using smartphones, exhibit greater proficiency in using smartphones, and demonstrate better judgement and differentiation ability in the face of good and bad health information online. From an age perspective, smartphone use exerted a greater promoting effect on the self-rated health of the 46–59-year-old group of middle-aged individuals. Middle-aged people are more accepting of new things and have fewer learning disabilities and technical barriers when using smartphones. Therefore, they use them more frequently to obtain all types of information and to maintain social contacts. However, for elderly people aged 60 years and older, there are certain difficulties in learning how to use smartphones, and they feel rejection and fear in the face of digital technology; therefore, these individuals are more likely to give up trying to integrate into digital life[56]. Their use of smartphones is more focussed on the basic functions of voice calling and maintaining necessary contact with the outside world[57], which brings very limited health effects. In addition, the positive effect of smartphone use on the self-rated health of middle-aged and elderly people with higher levels of education was more significant. A previous study revealed that middle-aged and elderly people with higher education levels can more easily cross the digital divide and integrate into the digital age[58], which is verified in this study. Middle-aged and elderly people with higher education levels possess relatively more abundant social resources and are more likely to fully utilize smartphones for social maintenance and access to health resources, thereby more likely improving their self-rated health.

Mediating effect of health-promoting behaviour on the relationship between smartphone use and the self-rated health of rural middle-aged and elderly people

It was also revealed that the health-promoting behaviour of rural middle-aged and elderly people exerted an important mediating effect on the relationship between smartphone use and self-rated health, which is similar to the results of previous studies[30, 36, 59–62]. This mediating effect can be explained by Andersen's Behavioural Model of Health Services Use[63]. This model emphasizes that personal propensity characteristics are correlated with health outcomes. Personal propensity characteristics can directly or indirectly affect health outcomes by influencing health behaviour[64]. In this study, smartphone use can be considered an individual tendency feature. According to this model, middle-aged and elderly people who use smartphones are more likely to actively acquire effective online health resources and online health knowledge through online media, thereby increasing their health management awareness, adopting sound health management behaviours, and improving their health-promoting behaviour, thus enhancing their self-rated health. Using data from a national comprehensive survey in China, Zhou et al. found that Internet use among older adults was positively associated with self-rated health and negatively associated with psychological sub-health, and that Internet use promotes health in older adults by facilitating access to health information, healthy lifestyles, and enhanced social interaction[30]. Similarly, Guo et al. reported that internet use among elderly people can increase the frequency of physical exercise, thereby improving their physical health[60]. In addition, numerous rural population-based studies have shown that healthy living habits and behaviours are the key factors for preventing and treating diseases and protecting health; moreover, the lifespan can be increased, and the risk of death can be reduced by shaping healthy lifestyles, such as favouring vegetables, physical activity, reading, etc.[65, 66].

Policy implications

Against the background of the convergence of digitalization and the ageing process, in this study, the relationships among smartphone use, health-promoting behaviour, and self-rated health of rural middle-aged and elderly people were analysed. The relevant conclusions have important policy implications for improving the health of rural middle-aged and elderly people and actively promoting healthy ageing in rural areas.

First, the construction of mobile networks in rural areas should be strengthened, and a suitable digital access environment should be created for elderly individuals. Compared with that in urban areas, mobile network infrastructure in rural areas is relatively underdeveloped, and the popularity of smartphones among middle-aged and elderly people in rural areas should be improved. It is recommended that the government implement a digital countryside construction strategy, increase infrastructure construction of rural mobile networks, create a favourable digital access environment for the use of smartphones by middle-aged and elderly people in rural areas, drive them to better integrate into digital life, and mitigate the deterioration of the health status of middle-aged and elderly people caused by the digital divide.

Second, smartphone education for middle-aged and elderly people in rural areas should be strengthened to establish an age-friendly digital society. Owing to the flat information characteristics of the digital age, smartphone use and internet access could compensate for the lack of health information acquisition abilities among middle-aged and elderly people in rural areas. To this end, the provision of internet education to rural middle-aged and elderly individuals should be strengthened to cultivate their basic digital abilities, such as network connections, information searches, and digital communications, so that they can use smartphones to obtain and understand more comprehensive health information and thus improve their health status and promote the development of healthy ageing in rural areas.

Third, we should focus on group differences and revealed the differentiated uses of smartphones by middle-aged and elderly individuals. While the smartphone penetration rate among middle-aged and elderly people in rural areas has steadily increased, a “targeted support” policy is recommended to provide differentiated digital resource services, with a focus on precise help for rural women, elderly people above a certain age, and disadvantaged middle-aged and elderly groups with low education levels. In addition, in the process of ageing-appropriate construction, internet-related companies are encouraged to customize the research and development of smartphone-related functions on the basis of sex, age, and education level of middle-aged and elderly people to create differentiated ageing-appropriate products to meet their smartphone needs.

Limitations

Although this paper provides a certain theoretical basis for the investigation of relevant variables, due to data limitations, only cross-sectional data from the 2020 CRRS were analysed. There is a lack of longitudinal observation data, and the time-varying pattern of the influence mechanism of smartphone use cannot be comprehensively captured. The second phase of the CRRS survey was completed in 2022. After the release of relevant data, the existing database can be expanded to form a panel dataset, which can improve the study of relevant mechanisms. Second, this study focused only on the self-rated health of middle-aged and elderly people. Currently, the understanding of healthy ageing is continuously increasing. Thais et al. proposed that healthy ageing includes physical health as well as other aspects, such as physical function, cognitive ability, and mental health dimensions[67]. Therefore, future measurements of health can be expanded, and studies can be performed in greater depth to enrich the research results related to healthy ageing.

CRRS：China Rural Revitalization Survey

Acknowledgments

We are very grateful the Rural Development Institute Chinese Academy of Social Sciences for the approval to use the 2022 CRRS data. We also thank the anonymous reviewers for their valuable comments.

Authors’ contributions

Theme: X.G and X.L. Methodology: X.G and X.L. Software: X.G and J.W. Data Curation: X.G and X.L. Original draft: X.G. Review and editing: X.G and J.W. Supervision and funding acquisition: J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Ministry of Education Humanities and Social Sciences Youth Project (Grant number 22YJCZH188) and Anhui Provincial Colleges and Universities Outstanding Youth Scientific Research Project (Grant number 2023AH030062)

Availability of data and materials

The data presented in this study are publicly available via the China Rural Revitalization Survey (CRRS) database. Access data online via http://rdi.cass.cn/ggl/202210/t20221024_5551642.shtml. Further queries can be directed to the authors.

Ethics approval and consent to participate

All experimental protocols of this study were approved by the Ethics Committee of Anhui Medical University and all methods were conducted according to the guidelines of the Declaration of Helsinki and relevant Chinese laws and regulations. This study confirms that informed consent was obtained from all participants and/or their legal guardians. Written informed consent was obtained from all participants before administering any study procedures.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Smartphone use, health-promoting behaviours and self-rated health among middle-aged and elderly people in rural areas

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Abstract

Objective

Methods

Results

Conclusion

Background

Data and methods

Data sources

Variable selection

Dependent variable

Independent variable

Mediating variable

Control variables

Data analysis

Results

Descriptive statistics

Baseline regression results

Robustness test

Heterogeneity analysis

Age heterogeneity analysis

Sex heterogeneity analysis

Education level heterogeneity analysis

Mechanism analysis

Note: , and denote significance at the 10%, 5% and 1% levels, respectively

Discussion

Smartphone use among rural middle-aged and elderly people directly reinforces health-promoting behaviour

Smartphone use among rural middle-aged and elderly people directly affects their self-rated health

Policy implications

Limitations

Abbreviations

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References

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