Analysis of factors influencing digital literacy of rural elderly people-evidence from China | BMC Public Health

Sample selection

To analyze the factors influencing digital literacy among elderly rural residents, it is essential to choose a representative sample of this population. The representativeness of the rural elderly sample was generally positively correlated with the total population of the region. Therefore, assuming that samples from more populous areas are more representative, the following sample selection is based on this assumption.Mainland China (excluding Taiwan, Hong Kong, and Macau) consists of 31 provinces, autonomous regions, and municipalities. These regions can be categorized as eastern, central, and western areas. To study the impact of digital literacy on the health status of elderly rural residents in China, elderly residents from one township in the eastern, central, and western regions were selected as research samples. combining both targeted and random selections. The targeted selection involved choosing the most populous provinces in each region: Guangdong in the eastern region, Henan in the central region, and Sichuan in the western region [25]. Within the most populous provinces, the county with the highest population was selected, and within that county, the most populous township was chosen as the regional sample. From these three sample townships, 240 elderly rural residents were randomly selected as participants.

The logic behind This sampling method is based on the assumption that elderly residents from the most populous administrative regions in eastern, central, and western China are representative of the sample. The selected sample areas were Qilin Township in Puning City, Guangdong Province (eastern region), Huzu Pu Township in Gushi County, Henan Province (central region), and Wangyang Township in Renshou County, Sichuan Province (western region). The population of Qilin Township is 134,800 [26], Huzu Pu Township has a population of 83,900 [27], and Wangyang Township has a population of 149,900 [28]. The total sample size for this study was 240 individuals, with samples drawn proportionally from the townships based on population size: 88 elderly residents from Qilin Township, 55 from Huzu Pu Township, and 97 from Wangyang Township. Among the 240 participants, there were 121 elderly men and 119 were older women.

Research method

To account for the educational background and knowledge levels of elderly rural residents, a qualitative research method was employed, specifically interviews. Interviews allowed for comprehensive observation and communication with elderly residents. The interviews were conducted in a structured manner. The content of the structured interviews was designed in advance based on the research objectives and included questions about the participants’ age, education level, family status, health condition, and digital Literacy. During the preparation phase, interviewers were trained to ensure consistency and effectiveness in data collection. After obtaining informed consent from the elderly rural residents, trained interviewers conducted face-to-face interviews with the participants using tablet-assisted personal interview techniques from June 6 to August 10, 2024. Participants provided informed consent, with consent obtained verbally. The consent process involved two interviewers: one recorded the verbal consent, while the other supervised. During or after data collection, the authors were able to access information identifying individual participants.

To analyze the factors influencing the digital literacy of elderly rural residents, it is necessary to first identify the categories of digital literacy and its influencing factors. After the analysis and research, the components of digital literacy were identified, including seven aspects: digital device operation, digital social interaction, digital finance, digital agriculture, digital cultural activities, digital security, and digital ethics. The influencing factors were categorized into six aspects: personal information such as age, marital status, education level, living conditions, income level, and health status.

In designing the interview structure to evaluat digital Literacy, the research focused on content related to digital device operation, digital social interaction, digital finance, and so on. The interview design included seven major categories and 11 subcategories, covering areas such as the daily operation and use of mobile phones and other electronic devices, information acquisition and transmission, digital information searching, video games, and online social interactions. The response options for the questions were as follows: 1 = frequently used, 2 = infrequently used, 3 = not used, and both 1 and 2 were categorized within the range of usage. For the interview structure regarding influencing factors, the content was designed around six aspects with 13 subcategories, age, marital status, education level, living conditions, income level, and health status. Toevaluate the health status of elderly rural residents, a self-assessment method was employed based on the “Chinese Healthy Elderly Standard” published by the National Health Commission of China [29]. Health status was categorized into three levels: healthy (80 ≤ health ≤ 100 points), Generally Healthy (60 ≤ Generally Healthy < 80 points), and unhealthy (< 60 points). The basic characteristics, digital literacy levels, and influencing factors of the interviewed elderly participants are summarized in Table 1.

This study used the internationally recognized SPSS software to analyze the correlation between digital literacy and its influencing factors among the elderly. Using SPSS, the relationship between digital literacy and influencing factors was examined to determine whether there was a correlation and the degree of that correlation. According to the analysis in Table 2, the average age of males was 71.26 years, females 70.19 years, and the overall average age of the study subjects was 70.71 years, with a standard deviation of 4.97. A P-value < 0.001 indicates that there is a significant difference between the average ages of the two groups, and this difference is highly unlikely to be due to chance. In terms of average years of education, rural males had an average of 9.8 years, females 9.1 years, and the elderly sample hads an average of 9.46 years, with a standard deviation of 0.03. A P-value < 0.001 indicates that the difference is highly statistically significant and unlikely to be due to chance. For self-rated health, the internationally recognized self-rated health value (1–5) was used [30]. The self-rated health value for males is 2.39, for females 2.48, and the overall average self-rated health value for the elderly sample is 2.45, with a standard deviation of 0.799. A P-value < 0.001 indicates that the difference in self-rated health values between the two groups is statistically significant, meaning that this difference is highly unlikely to be caused by random error. In terms of weekly digital information usage time, elderly males spend an average of 8.69 h per week, while females spend an average of 7.52 h per week. The overall average weekly digital information usage time for the elderly sample was 8.15 h, with a standard deviation of 9.42. A P-value < 0.001 indicates that the difference in weekly information technology usage time between males and females is highly statistically significant, meaning that this difference is not random. Therefore, the interview and survey data are of practical significance.