Association between mobile phone, self-efficacy and dependency among elderly people: a community-based study

Introduction

As the global population ages, the number of elderly individuals affected by declines in physical function and chronic disease is increasing rapidly (1,2). The prevalence of dependency also increases with age, especially among the elderly people with low social capital (3). Epidemiological studies have shown that dependency could also lead to increases in the consequences of diseases, such as major depression, heart disease, disability, and all-cause mortality (4-6). The prevalence of dependency is high among the elderly population in culturally traditional countries, especially China (7,8).

The etiological mechanism of dependency remains unclear; however, the consequences of dependency and poor health are more intimately associated with social, economic, and environmental factors, especially for the elderly people (9,10). With the expansion of the aging population, elderly individuals have complex and increased needs that require sustained input from community health or social services to support independent living (11). Although the traditional family unit has always been considered to be a major healthcare resource, the rapid development of society and economies has led more young people to move to urban areas or abroad, thus losing their ability to take care of elderly people in the community (12,13).

Recent studies have described the use of mobile phones in intervention strategies for smoking cessation, chronic disease management, maternal and child health promotion, and other fields of behavior and health (14-16). Mobile phone text messaging has been used as a healthcare tool in substance abuse, schizophrenia, affective disorders, and suicide prevention (17,18). More recently, WeChat-based telemedicine systems have been used to monitor and manage home-quarantined patients diagnosed with the novel coronavirus disease 2019 (COVID-19) (19). The increase in average life expectancy has consequently led to an increase in the demand for healthcare management strategies. As the use of mobile phones has increased, the development of health management services involving mobile phones has altered the focus of medical services from hospital visits to the management of health decisions made by individuals in their daily lives (20,21).

Many elderly individuals are concerned about the use of mobile phones due to technology anxiety and fear of working with new devices (22). However, in China, with the change in family and social structures, the number of elderly individuals living alone or those who do not live with their children is increasing. The number of mobile phone users is rapidly increasing among the elderly as a tool to stay connected with their children. A previous study reported that socially disadvantaged elderly individuals and those without families that provide unpaid care and sufficient income may fall into dependency earlier (23). The community environment is particularly important for the health of those living in rural areas; however, their community resources are clearly inadequate. A prospective study revealed that the availability of social resources is an important factor in improving individual dependency (24).

The present study aimed to clarify the association between mobile phone use and dependency among a sample of the elderly population. Suitable methods to reduce and delay dependency among elderly individuals were explored to alleviate the shortage of community service resources caused by the rapid expansion of the aging population. We hypothesized that through the effective use of mobile phones, elderly individuals would improve dependency, enhance autonomy and self-efficacy, and reduce the overuse of community service resources.

Methods

Subjects

This population-based, cross-sectional study was conducted to assess the health and health-related accessibility of resources and services among elderly individuals living in rural and urban areas. A total of 2,195 community residents ≥60 years of age were selected using a multistage sampling design. Sampling was conducted in 4 provinces of China (Zhejiang, Heilongjiang, Xinjiang, and Sichuan) from July 2020 to December 2023. The secondary sampling cities included Hangzhou, Harbin, Tulufan, and Chongqing, each representing 1 of the 4 sampled provinces. The third sampling unit comprised 33 locations in the secondary sampling cities. The sample size was determined using the event per variable method used in this study. Participants who were unable to complete the questionnaire were excluded. Detailed sampling is illustrated in the flow-diagram presented in Figure 1. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Informed consent was taken from all the participants. This study was approved by the Institutional Review Board of the School of Medicine, Zhejiang University (No. ZGL201909-10).

Figure 1 Flowchart of participant.

Data collection

All data were collected through in-person interviews using structured questionnaires. The questionnaire included 578 items, and the main contents included categories such as demographics, lifestyle and behavioral habits, self-reported chronic diseases and general health status (25), environmental and community health service resources (26), psychological and cognitive function assessment, and assessment of the activities of daily living. The interviews lasted for approximately 45–60 min for most participants.

Characteristic variables

The general characteristics of the participants included age, sex, marital status, income, living arrangement, educational level, self-reported health status, and daily life and behavioral habits. The use of mobile phones was included in the behavioral habits section, and the questions were as follows: “Do you use a mobile phone?”, “Do you use mobile phone for special purpose products for the elderly people or ordinary model?”, “Do you access the internet from your mobile phone?”, and “What do you usually use your mobile phone for?”. The responses included the following: “mobile phone call”; “text message”; “WeChat”; “health management”; “online shopping”; “watch news or entertainment programs”; and “other”. The frequency of mobile phone use was assessed according to the response to the question: “How many times did you talk to friends, relatives, or others in the last week?”

Measurements

Dependency

The Chinese version of the standardized Minnesota Multiphasic Personality Inventory-II was used in this study to assess dependency among elderly individuals (27). The dependency scale includes 57 items. Raw scores were calculated and converted into standardized T-scores. Dependency was defined as a standardized T-score ≥60 points for all participants. The test-retest reliability was 0.81 among females and 0.67 among males.

Self-efficacy

General self-efficacy was assessed using the Chinese version of the General Self-Efficacy Scale (GSES). The GSES consists of 10 items using a four-point rating scale, for a total of 10–40 points, with a Cronbach’s alpha of 0.89. Higher scores indicate a higher level of self-efficacy (28).

Other measurements

The revised Eysenck Personality Questionnaire (EPQ) Short Scale for the Chinese comprised 48 items and 4 scales. Participant personality characteristics were measured and the Cronbach’s alpha is 0.76 (29). Depressive symptoms were measured using the 15-item Geriatric Depression Scale, with a Cronbach’s α of 0.75. The scores range from 0–15 points, with 5 points indicating depressive symptoms and a higher score indicating a higher level of depression (30). Social support was assessed using the Chinese version of the Older Americans’ Resources and Services Scale (OARS). The OARS comprises three dimensions: social interactions, interpersonal relations, and family support. The Cronbach’s alpha coefficients ranged from 0.61 to 0.83. The total score is obtained by summing the ratings across these dimensions. Higher scores reflect greater levels of social support (31).

Statistical analysis

Analyses were performed using data from 2,195 participants who completed the questionnaires and provided dependency assessment data. General characteristics of the study participants are expressed as percentage. Chi-squared tests were used for the univariate analysis, with mobile phone status as an independent categorical variable.

Two separate cumulative logistic regression models were used to evaluate the association between the dependency on and use of mobile phones and community service resources. Dependency scores were treated as ordinal variables. If the participant’s dependency score was <40 points, it was expressed as “0” in the dependent variable of the logistic regression model; otherwise, it was expressed as “1” if their score was 40–59 points and “2” if ≥60 points. In model 1, the frequency of mobile phone use and community service resources were considered independent variables and were added to the model via a stepwise method. Model 2 included variables related to mobile phone use in terms of three aspects: model of mobile phone; Internet-enabled phone; and some functions usually used, including mobile phone calls, text messaging, WeChat, health management platforms, online shopping platforms, and news or entertainment. Both models were adjusted for age, sex, educational level, marital status, smoking status, alcohol use, physical activity, individual income, chronic disease status, and EPQ scores to reduce possible biases caused by confounding factors. Binary logistic regression was used to identify the association between mobile phone use and community service resources.

Analysis of covariance (ANCOVA) was used to evaluate the levels of self-efficacy based on differences in the frequency of mobile phone use. Normality and variance homogeneity tests were performed. In the ANCOVA model, the self-efficacy scores were divided into 4 categories based on the frequency of mobile phone use.

All analyses were performed at a significance level of P<0.05, with an overall alpha of 0.05, using SAS version 9.4 (SAS Institute, Cary, NC, USA) for Windows (Microsoft Corporation, Redmond, WA, USA).

Results

The demographic characteristics of the participants are summarized in Table 1. Among the participants, 42.8% were >70 years of age, 37.9% were male, and 62.1% were female. The proportion of participants with a low income was high (29.8%), and 91.5% of elderly individuals reported that they used mobile phones, with 44.7% using them ≥1 time(s) per day.

The characteristics of mobile phone use, categorized according to dependency scores, are summarized in Table 2. The results of univariate analysis revealed that participants who had a mobile phone of the common type or with Internet-enabled function(s) had significantly lower dependency scores than those who had a mobile phone designed for elderly individuals or those without the Internet. Participants who usually used the main functions on their mobile phones, including mobile phone calls, WeChat, health management platforms, online shopping platforms, and news or entertainment, had a significantly lower rate of high dependency scores than others. There was no significant difference in the proportions of participants with high and low dependency scores who regularly used text messages.

The odds ratios (ORs) for dependency associated with the frequency of mobile phone use obtained via cumulative logistic regression analysis are summarized in Table 3. In model 1, the frequency of mobile phone use was significantly and negatively associated with the levels of dependency scores, with an OR of 0.87 [95% confidence interval (CI): 0.76–0.99; P=0.04]. The received times of community health services was significantly and positively associated with the levels of dependency scores, with an OR of 1.22 (95% CI: 1.07–1.38; P=0.002). Participants who responded “yes” to questions regarding the use of electronic health records, geriatric wards, and short-term care homes exhibited a positive association with levels of dependency scores, with ORs of 2.16 (95% CI: 1.64–2.84; P<0.001), 1.81 (95% CI: 1.18–2.77; P=0.007), and 1.73 (95% CI: 1.13–2.65; P=0.01), respectively. These associations were maintained in model 2 after adding the variables of participants who usually used the main functions on their mobile phones, such as mobile phone calls, WeChat, health management platforms, online shopping platforms, and news or entertainment. Participants who responded “yes” to the question “mainly use mobile phone to call” exhibited a significant association with lower levels of dependency scores than those who responded “no”, with an OR of 0.30 (95% CI: 0.15–0.63; P=0.001).

Results of analysis of the community health service factors related to mobile phone use are reported in Table 4. The question “usually use mobile phone to call” was significantly and negatively associated with the times of received community health services (OR, 0.63; 95% CI: 0.44–0.91) and services of the geriatric ward (OR, 0.18; 95% CI: 0.07–0.48), and positively associated with services for chronic severe disease (OR, 5.69; 95% CI: 1.68–19.17).

The mean self-efficacy scores for different frequencies of mobile phone use, obtained via ANCOVA, are presented in Figure 2, and were 23.49, 24.92, 25.69, and 26.28 in the 4 groups “0 time”, “1 time”, “2–6 times”, and “>6 times” per week, which resulted in a significant linear trend (P<0.001).

Figure 2 The means of GSES score for frequency of mobile phone use by ANCOVA model. GSES, General Self-Efficacy Scale; FUMP, frequency of mobile phone use; ANCOVA, analysis of covariance.

Discussion

The initial hypothesis of the present study was tested using self-efficacy measurement tools and a possible method for improving the dependency of elderly individuals and reducing the use of community health services was explored. This study revealed that the frequency of mobile phone use was significantly associated with dependency among elderly individuals. Community health service resources were associated with the use of mobile phones based on the low usage percentage for community health services such as geriatric wards. In addition, mobile phone use was associated with a high level of self-efficacy.

In the past decade, with the rapid development of mobile phone technology, health management-related applications have been widely developed and used (32,33). Although most previous studies have shown that text messaging is a good tool for mental health intervention, in the present study, 95.8% of participants reported that they mostly used mobile phones to call others, with only 37.1% using mobile phones to send text messages, and 10.5% using the health management platform of the mobile phone. Similar results have been reported in other studies, in which elderly individuals were more likely to only use the phone call function, whereas those with higher education were more likely to use a smartphone. A pilot study reported that that only 35% of participants favored text messaging and 54% favored voice calls (34).

Study has shown that mobile phone interventions can serve as highly cost-effective mental health promotion tools for several patient populations (35). These interventions have been used as components of positive psychological and cognitive-behavioral intervention strategies for non-clinical populations through automated chatbot applications. The present study found that a high frequency of mobile phone use was significantly associated with a low level of dependency among older adults. Moreover, the participants who mainly used their mobile phone to call had significantly lower levels of dependency than those who responded “no”. These results suggest that mobile phone calls can be used efficiently as a simple intervention tool to reduce the prevalence of dependency among older adults. In addition, 50% of the participants reported that they had lower incomes than other income groups. A high prevalence of dependency exists among the elderly in low-income and vulnerable groups. Therefore, mobile phone calls could also be considered an accessible intervention tool for dependency among low-income elderly individuals. With the rapid development of mobile application software technology, some may neglect the use of the most basic functions of mobile phones; however, mobile phone calls are very important to the elderly.

Several previous studies have reported that mobile application programs could promote patient self-efficacy (36,37). The results revealed that the use of mobile phones was significantly associated with reduced utilization of community health services and geriatric wards, but did not reduce the use of services for patients with chronic severe diseases. In addition, a high frequency of mobile phone use was associated with high self-efficacy scores among the study participants. Although a conclusion, such as the use of mobile phones could reduce the dependency on elderly individuals and the utilization of community health service resources by promoting self-efficacy, could not be directly drawn, some studies have shown that phone calls are also a means of obtaining social support. However, based on the current analysis and previous studies, the promotion of individual self-efficacy and partial improvement in the dependency of elderly individuals through mobile phone use could, at least, be partially explained.

Presently, no reliable methods on how to reduce dependency among elderly individuals using a low-cost and accessible strategy are available. To the best of our knowledge, this study is the first to explore the association between mobile phone use and dependency, utilization of community health services, and self-efficacy among older adults. Compared with previous clinical and experimental studies, this community-based study had a relatively large sample size. This verified that mobile phone use as a tool for dependency intervention has universal adaptability in the general elderly population. Full data were collected to facilitate the investigation of additional related factors and control for potential risk factors in the analysis.

This study has a major limitation that must be addressed. Associations between dependency, mobile phone use, and self-efficacy were observed through multiple analyses; however, causality could not be determined due to the cross-sectional design. Additionally, during the COVID-19 pandemic, we excluded elderly individuals with severe dysfunction and potential risks, which may have introduced exclusion bias.

Conclusions

In conclusion, the use of mobile phone has an important effect on dependency among elderly people. This study indicated that the high frequency of mobile phone calls was significantly associated with low level of dependency and reduced utilization of community health service resources. Although this study fully considered the association of dependency and mobile phone use with the related factors and demonstrated that mobile phone use was associated with increased self-efficacy, the causal effect was not identifiable, through which mobile phone use increased self-efficacy and reduced dependency and the utilization of community health service resources.

Acknowledgments

None.

Footnote

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-35/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-35/prf

Funding: This work was supported by the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (No. 2020E10004), and in part by the National Natural Science Foundation of China under Grant (No. 62173302).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-24-35/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Informed consent was taken from all the participants. This study was approved by the Institutional Review Board of the School of Medicine, Zhejiang University (No. ZGL201909-10).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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