Abstract
Objective
This study aims to examine the relationships among perceived burdensomeness, technology use habits, and activities of daily living (ADL) in older adults residing in rural areas.
Methods
This descriptive correlational study was conducted with 384 older adults living in rural areas in two different regions of Turkey. Data were collected using an Information Form, the Geriatric Perceived Burdensomeness Scale, the Barthel Index for Activities of Daily Living, and the Technology Use Habits Scale.
Results
Perceived burdensomeness was moderately and negatively correlated with ADL (r = − 0.484, p < 0.001), whereas technology use habits were weakly but positively correlated with BADL (r = 0.192, p < 0.001). In the hierarchical regression analysis, technology use habits showed a positive and statistically significant association with ADL in the initial model (β = 0.189, p < 0.001). When perceived burdensomeness was added to the model, it emerged as a strong negative predictor of ADL (β = −0.497, p < 0.001), substantially increasing the explained variance. In the fully adjusted model, perceived burdensomeness, age, digital device use, and duration of digital device use were significantly associated with ADL, whereas age did not significantly affect the relationship between perceived burdensomeness and ADL. Overall, perceived burdensomeness and technology use habits were identified as key factors related to functional independence among rural older adults.
Conclusion
The findings of this study indicate that perceived burdensomeness and technology use habits are closely associated with independence in activities of daily living among older adults living in rural areas. These findings indicate an association between technology use and perceived burdensomeness and provide a foundation for future research in nursing practice and rural health services. Future research is recommended to focus on evaluating technology-based and psychosocial interventions that support independence and psychosocial well-being in rural older populations.
Clinical trial number
Not applicable.
Keywords: Activities of daily living, Burden, Older adults, Technology, Rural
Introduction
The growing global elderly population has highlighted the maintenance of independence in Activities of Daily Living (ADL) as a key indicator of healthy aging. Independence in ADL not only supports physical functioning in older adults but also fosters social participation, autonomy, and psychological well-being [1]. Conversely, limitations in ADL are associated with reduced quality of life, increased healthcare expenditures, and a higher risk of institutional care [2]. Each year, between 13% and 24% of older adults who previously maintained independence in activities of daily living develop partial or complete dependency due to physical, cognitive, or environmental changes [3].
This process of dependency is influenced not only by physiological aging but also by individuals’ sociodemographic and environmental conditions [4]. Older adults’ ability to sustain daily living activities depends on the interaction of multidimensional factors, including health status, cognitive competence, social relationships, income, and environmental support. Accordingly, age, gender, educational level, economic resources, social support, and environmental conditions significantly influence functional independence [5, 6].
Although ADL limitations pose a significant threat to the quality of life among older adults worldwide, those living in rural areas experience these challenges more acutely [7, 8]. Socioeconomic disadvantages, inadequate healthcare infrastructure, and lifestyle factors render rural older adults more vulnerable to health problems [9]. Long distances, transportation difficulties, and infrastructural deficiencies restrict access to healthcare and rehabilitation services. Additionally, low income and education levels hinder effective disease management and the maintenance of independent living skills [9, 10]. These structural and environmental limitations negatively affect not only the physical functioning of older adults but also their psychological well-being [8]. In rural life, limited social networks, weak family support, and insufficient community-based resources intensify the psychosocial consequences of dependency, particularly the sense of being a burden and the perception of loneliness [11].
One of these psychosocial outcomes is perceived burdensomeness, which refers to an individual’s perception of being a burden to others or to society [12]. Research has shown that this feeling is significantly associated with depression, anxiety, loss of self-esteem, and suicidal ideation [13–15]. Among older adults living in rural areas, ADL limitations and dependency can undermine autonomy and perceived usefulness, thereby triggering perceived burdensomeness. Indeed, previous studies have demonstrated that as functional independence decreases, perceived burdensomeness increases, negatively affecting mental health, reducing well-being, and diminishing motivation to participate in daily life [16, 17].
In the face of these psychosocial challenges and the risk of losing independence, new approaches are needed to support older adults. In this context, technology has emerged as a valuable tool to help older individuals achieve greater independence in daily life and enhance their quality of life [18]. Telehealth services, wearable monitoring devices, and digital communication platforms can strengthen health management, facilitate social interaction, and support cognitive functioning [19, 20]. However, these potential benefits are often limited for older adults residing in rural areas. Insufficient internet infrastructure, low digital literacy, and inequalities in digital access hinder rural older adults from fully utilizing technology [19, 21]. Moreover, digital exclusion has been shown to increase social isolation and adversely affect health outcomes [22–24]. Nevertheless, when appropriate training, community support, and age-friendly designs are provided, older adults in rural areas can adopt technology and improve both their independence and social connectedness [25].
Thus, barriers to technology access and use have significant physical and psychosocial consequences for the rural aging population. Most existing studies, however, have focused on older adults living in urban areas [26, 27], and no research has yet directly examined the relationships among perceived burdensomeness, technology use, and ADL in rural older adults.
Accordingly, the present study aims to investigate the relationships between perceived burdensomeness, technology use habits, and activities of daily living among older adults residing in rural areas. By addressing this focus, the study seeks to fill a critical gap in the literature and contribute to the development of interventions that promote both functional independence and psychosocial well-being in the rural elderly population.
Methods
Study design and sample
This descriptive correlational study was conducted with 384 older adults living in rural districts and villages of two provinces in the Eastern Black Sea and Central Anatolia regions. A post-hoc power analysis was conducted using G*Power version 3.1.9.7 [28] to confirm that the obtained sample size was sufficient for multiple linear regression analysis. Based on a medium effect size (f² = 0.15), α = 0.05, and four predictors, the achieved power was calculated as 0.95, indicating that the final sample of 384 participants provided sufficient statistical power to detect significant associations among the study variables. The inclusion criteria were older adults aged 65 years or older, capable of understanding and answering the questions, and voluntarily agreeing to participate in the study. Exclusion criteria were older adults living in urban centers and those who were unable to understand and answer the questions due to cognitive or communicative disabilities. The provinces included in the study were selected based on the researchers’ places of residence, which provided direct access to the rural elderly population. This facilitated a data collection process that was more efficient, reliable, and adapted to local conditions. In both provinces, approximately 8–10 villages (4–5 from each province) were selected through purposive sampling, considering criteria such as population density, ease of transportation, and the presence of primary healthcare units. Considering the low population density, transportation challenges, and the necessity for cultural engagement in rural areas, this sampling method was considered appropriate.
Participants were recruited with the assistance of village headmen, family health centers, and local social networks. During data collection, individuals aged 65 years and older were reached in communal gathering places within the villages (such as coffeehouses, village halls, mosque courtyards, and village squares) as well as in other easily accessible social settings; home visits were conducted when necessary. To minimize selection bias and enhance external validity, data were collected from villages with diverse socioeconomic and infrastructural characteristics, and efforts were made to ensure variation among participants in terms of age, gender, education, and income levels.
Data collection tools
Data were collected using the Information Form, Geriatric Perceived Burdensomeness Scale (GFBS), Barthel Index for Activities of Daily Living (BADL), and Technology Use Habits Scale (TUHS).
Information form
This form was developed by the researchers in accordance with the relevant literature [13, 22, 29, 30]. It comprises nine items assessing sociodemographic characteristics (age, gender, educational status, marital status, income level), current health conditions (chronic diseases), and technology use (use of a technological device, type of device used, duration of use) of older adults (Suppl. File S1). To ensure clarity and face validity, the form was reviewed by two faculty members specialized in geriatric nursing and public health, and minor wording revisions were made based on their feedback prior to data collection. The final version of the form was used in all interviews.
Geriatric perceived burdensomeness scale
The scale used to assess perceived burdensomeness among older adults was developed by Lutz et al. [15] and adapted into Turkish by Çekiç and Yüksel in 2024 [29]. It is a 5-point Likert-type scale consisting of 25 items scored from “1- Strongly Disagree” to “5- Strongly Agree.” The minimum and maximum scores that can be obtained from the scale are 25 and 125, respectively. As the scale score increases, the feeling of geriatric burdensomeness increases. The original Cronbach’s alpha coefficient was 0.95 [29]. In this study, Cronbach’s alpha coefficient was found to be 0.95.
Barthel index for activities of daily living
The index was developed by Mahoney and Barthel in 1965 [31], and its Turkish adaptation’s validity and reliability were studied by Küçükdeveci et al. (2000) [32]. This index was used to determine the independence level of individuals in activities of daily living. Barthel Index scores vary between 0 and 100 points; 0–20 points indicate complete dependence, 21–61 points indicate heavy dependence, 62–90 points indicate moderate dependence, 91–99 points indicate mild dependence, and 100 points indicate independence. Studies using the Barthel Index take 60 points as the threshold, with scores above 60 indicating independent functioning [32]. In this study, Cronbach’s alpha coefficient of the scale was calculated as 0.90.
Technology use habits scale
The Technology Use Habits Scale was developed by Küçükvardar (2020) to assess individuals’ technology use habits [30]. It is a five-point Likert-type scale consisting of 32 items and six factors: use and time, technological invasion, technology adoption, technological obsession, communication cycle, and spiritual/mental variables. The total scores obtained from the scale determine the addiction risk levels; 32–57 points are considered as no addiction risk, 58–83 points as low addiction risk, 84–109 points as moderate addiction risk, 110–135 points as high addiction risk, and 136–160 points as very high addiction risk. In the study in which the scale was developed, the internal consistency coefficient (Cronbach’s alpha) was reported as 0.92 [30]. In this study, the Cronbach’s alpha coefficient of the scale was found to be 0.96.
Data collection
Data were collected between December 2024 and February 2025 from older adults living in rural districts and villages of two provinces in the Eastern Black Sea and Central Anatolia regions of Turkey. Face-to-face interviews were conducted with older adults who met the inclusion criteria and who provided informed consent to participate. The data collection process was carried out by the researchers either in participants’ homes or village health centers and village heads’ offices when they were easily accessible. The interviews lasted an average of 20 min. The scale items were read aloud by the researchers to facilitate the participants’ comprehension, with additional clarifications provided as needed. To minimize interviewer bias and ensure consistency, all interviews were conducted by the same researcher. When clarification was required, neutral, standardized phrasing was employed, avoiding any examples or interpretations that might bias participants’ responses.
Statistical analysis
For data analysis, the IBM International Business Machinery (IBM) Social Sciences Statistics Package (SPSS) version 25.0 (Armonk, NY: IBM Corp., 2022) was used. To assess the suitability of parametric tests, normality was evaluated using skewness and kurtosis coefficients. Normality was assessed according to the criterion that the skewness and kurtosis values were within the ± 2 range [33]. An independent samples t-test was used to assess whether there was a statistically significant difference between two groups. A one-way ANOVA test was applied to determine the difference between more than two groups. Pearson correlation analysis was used to examine whether there was a linear relationship between the variables. Factors affecting perceived burden, activities of daily living, and technology use habits in the elderly were determined using multiple linear regression analysis. In the multiple linear regression analysis, BADL was defined as the dependent variable representing the level of functional independence of older adults. The independent variables were determined as GFBS and TUHS. The control variables were evaluated as age, presence of chronic disease, digital device use, and digital device use duration. Before the analysis, the assumptions of multiple linear regression were examined. Multicollinearity was assessed using the Variance Inflation Factor (VIF), and all values were found to be below 2.0. The Durbin-Watson statistic (1.747) showed no autocorrelation. Model fit was assessed using R, R², F statistics and standardized β coefficients. A p-value less than 0.05 was considered statistically significant.
Results
Among older adults, 64.3% were aged 65–74 years, 52.9% were female, and 36.7% had completed primary school. 88.3% were married, 72.4% had middle income, and 76% had at least one chronic disease. 78% used a digital device, and 41.4% used a digital device for 0–30 min daily (see Table 1).
Table 1.
Sociodemographic characteristics of older adults living in rural areas (n = 384)
| n | % | |
|---|---|---|
| Age | ||
| 65–74 years | 247 | 64.3 |
| 75–84 years | 112 | 29.2 |
| ≥ 85 years | 25 | 6.5 |
| Gender | ||
| Female | 203 | 52.9 |
| Male | 181 | 47.1 |
| t/p | ||
| Education level | ||
| No formal education | 109 | 28.4 |
| Literate | 71 | 18.5 |
| Primary education | 141 | 36.7 |
| Secondary education | 33 | 8.6 |
| High School | 25 | 6.5 |
| University education | 5 | 1.3 |
| Marital status | ||
| Single | 45 | 11.7 |
| Married | 339 | 88.3 |
| t/p | ||
| Income level | ||
| Low | 54 | 14.1 |
| Middle | 278 | 72.4 |
| High | 52 | 13.5 |
| Presence of other chronic diseases | ||
| Yes | 292 | 76.0 |
| No | 92 | 24.0 |
| t/p | ||
| Use of digital devices | ||
| Yes | 302 | 78.6 |
| No | 82 | 21.4 |
| t/p | ||
| Duration of digital device use | ||
| Not using | 24 | 6.3 |
| 0–30 min | 159 | 41.4 |
| 30–60 min | 111 | 28.9 |
| 60–90 min | 59 | 15.4 |
| 90–120 min | 29 | 7.6 |
| 120 min and above | 2 | 0.5 |
When the sociodemographic characteristics of older adults were compared with the scores of GFBS, BADL, and TUHS, the mean scores of GFBS and BADL differed significantly among all age groups (p < 0.05). The mean TUHS scores were higher in the 65–74 age group than in the 75–84 and ≥ 85 age groups (p < 0.05). In addition, the TUHS scores of older adults with undergraduate/graduate, high school, and secondary school graduates were statistically significantly higher than those of illiterates (p < 0.05). Older adults with moderate or low income had higher mean GFBS scores than those with high income (p < 0.05). The mean GFBS scores of older adults with another chronic disease and the mean BADL scores of those without a chronic disease were significantly higher (p < 0.05). Older adults who did not use any digital devices had significantly higher GFBS scores than those who did, and the mean BADL and TUHS scores of older adults who used any digital tools were significantly higher than those who did not (p < 0.05). There was a significant difference between the duration of digital tool use and the mean scores of GFBS, BADL, and TUHS (p < 0.05). No difference was found between other sociodemographic characteristics and the GFBS, BADL, and TUHS scores of older adults (see Table 2).
Table 2.
Comparison of sociodemographic characteristics and mean scale scores of older adults in rural areas (n = 384)
| GFBS | BADL | TUHS | |
|---|---|---|---|
| Age | Mean ± SD | Mean ± SD | Mean ± SD |
| 65–74 years A | 63.19 ± 17.95 | 77.32 ± 21.04 | 66.82 ± 21.81 |
| 75–84 years B | 71.67 ± 18.22 | 55.58 ± 27.96 | 59.91 ± 20.13 |
| ≥ 85 years C | 82.84 ± 18.01 | 26.60 ± 29.18 | 52.24 ± 24.44 |
|
F/p * |
18.909/0.000 C > A,B; B > A |
71.877/0.000 A > B,C; B > C |
7.865/0.000 A > B,C |
| Gender | |||
| Female | 67.67 ± 18.01 | 69.90 ± 26.34 | 65.01 ± 23.06 |
| Male | 66.13 ± 19.77 | 65.19 ± 29.38 | 62.55 ± 20.48 |
| t/p | 0.794/0.428 | 1.655/0.099 | 1.100/0.272 |
| Education level | |||
| No formal education D | 70.48 ± 19.35 | 62.61 ± 28.43 | 57.59 ± 20.59 |
| Literate E | 69.08 ± 20.57 | 64.50 ± 31.53 | 63.49 ± 20.92 |
| Primary education F | 65.22 ± 17.86 | 71.84 ± 25.36 | 63.28 ± 22.08 |
| Secondary education G | 63.30 ± 15.89 | 74.39 ± 23.74 | 74.93 ± 19.36 |
| High School H | 62.68 ± 19.11 | 65.00 ± 31.02 | 76.04 ± 20.18 |
| University education I | 53.40 ± 12.58 | 75.00 ± 20.91 | 87.80 ± 21.89 |
|
F/p * |
2.239/0.049 | 2.058/0.070 |
6.704/0.000 G, H,I > D |
| Marital status | |||
| Single | 69.64 ± 18.73 | 65.33 ± 24.91 | 64.84 ± 20.65 |
| Married | 66.59 ± 18.87 | 67.99 ± 28.27 | 63.72 ± 22.08 |
| t/p | 1.021/0.308 | -0.601/0.548 | 0.321/0.749 |
| Income | |||
| LowJ | 72.88 ± 19.94 | 65.00 ± 27.31 | 60.48 ± 27.75 |
| Middle K | 67.45 ± 17.93 | 67.96 ± 27.51 | 65.12 ± 20.84 |
| HighL | 58.07 ± 19.80 | 68.94 ± 30.69 | 60.61 ± 20.21 |
|
F/p * |
8.883/0.000 J, K > L |
0.316/0.729 | 1.682/0.187 |
| Presence of other chronic diseases | |||
| Yes | 68.30 ± 18.33 | 65.80 ± 28.89 | 64.55 ± 22.34 |
| No | 62.65 ± 19.92 | 73.64 ± 23.54 | 61.65 ± 20.38 |
| t/p | 2.523/0.012 | -2.365/0.019 | 1.109/0.268 |
| Use of digital devices | |||
| Yes | 65.07 ± 18.47 | 73.64 ± 23.65 | 65.86 ± 21.40 |
| No | 73.82 ± 18.77 | 45.73 ± 31.24 | 56.47 ± 22.21 |
| t/p | -3.790/0.000 | 8.805/0.000 | 3.493/0.001 |
| Duration of digital device use | |||
| Not usingM | 74.66 ± 16.24 | 48.12 ± 27.45 | 55.00 ± 17.82 |
| 0–30 minN | 69.28 ± 18.91 | 60.22 ± 30.78 | 56.77 ± 21.07 |
| 30–60 minO | 62.91 ± 18.37 | 75.04 ± 21.53 | 68.65 ± 19.90 |
| 60–90 minP | 64.72 ± 17.07 | 78.22 ± 20.86 | 68.98 ± 19.88 |
| 90–120 minR | 68.55 ± 22.88 | 73.10 ± 27.52 | 79.79 ± 24.78 |
| 120 min and aboveS | 54.00 ± 11.31 | 97.50 ± 3.53 | 85.00 ± 12.72 |
|
F/p * |
2.763/0.018 |
9.497/0.000 O, P,R > M, O, P > N |
10.411/0.000 O, R > M, O, P,R > N |
F one way ANOVA test, SD Standard Deviation, t independent t-test, *, Tukey test
The mean total score of the GFBS was 66.94 ± 18.85, the mean BADL score was 73.63 ± 26.92, and the mean TUHS score was 63.85 ± 21.89 (see Table 3).
Table 3.
Mean scores of the scales (n=384)
| Scales | Mean ± SD | Min-Max |
|---|---|---|
| GFBS | 66.94 ± 18.85 | 25–125 |
| BADL | 73.63 ± 26.92 | 10–100 |
| TUHS | 63.85 ± 21.89 | 32–157 |
GFBS Geriatric Perceived Burdensomeness Scale, BADL Barthel Index for Activities of Daily Living, TUHS Technology Use Habits Scale
A moderate and negative relationship was found between the GFBS and BADL scores of older adults (r=-0.484; p < 0.001). In addition, there was a weak and positive correlation between TUHS and BADL scores (r = 0.192; p < 0.001). No correlation was found between the GFBS and TUHS scores (r = 0.035; p > 0.05) (see Table 4).
Table 4.
The relationship between the mean scores of the scales (n = 384)
| GFBS | BADL | TUHS | ||
|---|---|---|---|---|
| GFBS | r | 1 | ||
| p | ||||
| BADL | r | -0.484 | 1 | |
| p | 0.000 | |||
| TUHS | r | 0.035 | 0.192 | 1 |
| p | 0.498 | 0.000 |
GFBS Geriatric Perceived burdensomeness Scale, BADL Barthel Index for Activities of Daily Living, TUHS Technology Use Habits Scale
In Table 5, a two-step regression analysis was conducted to examine the effects of TUHS and GFBS on BADL. In Model 1, only TUHS was entered into the model. The results indicate that technology use has a positive and statistically significant effect on activities of daily living (β = 0.189, p < 0.001). This model explains approximately 3.5% of the variance in BADL (R² = 0.035).
Table 5.
Regression analysis results for the effects of technology use and emotional burden on activities of daily living (n = 384)
| Model | Standardized Coefficients | t | p | 95,0% Confidence Interval | ||
|---|---|---|---|---|---|---|
| Beta | Lower | Upper | ||||
| 1 | (Constant) | 52.327 | 12.116 | < 0.001 | 43.836 | 60.819 |
| TUHS | 0.189 | 3.758 | < 0.001 | 0.115 | 0.366 | |
| 2 | (Constant) | 116.197 | 17.888 | < 0.001 | 89.140 | 111.156 |
| GFBS | − 0.497 | -11.453 | < 0.001 | − 0.854 | − 0.595 | |
Dependent variable: BADLI, Model 1: R: 0.189, R2: 0.035, Adjusted R²: 0.033, Model 2: R: 0.490, R2: 0.240, Adjusted R²: 0.238
In Model 2, GFBS was added to the model. In this model, perceived emotional burden was found to have a strong and negative effect on BADL (β = −0.497, p < 0.001). With the inclusion of perceived burden, the explained variance increased substantially, and the model accounted for approximately 24% of the variance in BADL (R² = 0.240). This increase indicates that perceived burden is a significant predictor of activities of daily living (see Table 5).
As presented in Table 6, a multiple linear regression analysis was conducted to examine the effects of GFBS and TUHS on BADLI while controlling for age, presence of other chronic diseases, digital device use, and duration of digital device use. Compared with the reference group (65–74 years), individuals aged 75–84 years showed significantly lower BADLI scores (B = − 13.41, β = −0.48, p < 0.001), and this decrease was more pronounced among those aged 85 years and above (B = − 30.12, β = −1.08, p < 0.001), indicating a substantial decline in activities of daily living with increasing age. The presence of other chronic diseases was not significantly associated with BADLI (B = 3.34, β = 0.12, p = 0.189). In contrast, individuals who did not use a digital tool had significantly lower BADLI scores compared with those who used digital tools (B = − 15.77, β = −0.57, p < 0.001). The duration of digital device use was not a significant predictor of BADLI across any usage category when compared with non-users (all p > 0.05). Regarding the main predictors, GFBS was significantly and negatively associated with BADLI (B = − 0.51, β = −0.34, p < 0.001), indicating that higher levels of perceived burden were associated with poorer activities of daily living. Conversely, TUHS showed a positive and statistically significant association with BADLI (B = 0.11, β = 0.08, p = 0.044), suggesting that higher levels of technology use were related to better functional outcomes (see Table 6).
Table 6.
The effects of emotional burden and technology use after controlling for conditional variables (n = 384)
| Predictors | p | Beta | B | 95,0% Confidence Interval | ||||
|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||
| Intercept | < 0.001 | 107.219 | 93.032 | 121.406 | ||||
| Age (Ref: 65–74) | ||||||||
| 75–84 | < 0.001 | -0.481 | -13.414 | -18.305 | -8.523 | |||
| 85 ve üzeri | < 0.001 | -1.080 | -30.115 | -39.460 | -20.770 | |||
| Other chronic diseases (Ref: Yes) | ||||||||
| No | 0.189 | 0.119 | 3.342 | -1.65545 | 8.339 | |||
| Do you use a digital tool? (Ref: Yes) | ||||||||
| No | < 0.001 | -0.565 | -15.769 | -22.05617 | -9.482 | |||
| How long do you use the digital tool? (Ref: Don’t use) | ||||||||
| 30 min. and below | 0.199 | -0.233 | -6.519 | -16.474 | 3.436 | |||
| 31–60 min. | 0.512 | -0.129 | -3.608 | -14.419 | 7.204 | |||
| 61–90 min. | 0.940 | -0.015 | -0.444 | -11.972 | 11.084 | |||
| 91–120 min. | 0.547 | -0.141 | -3.956 | -16.853 | 8.943 | |||
| 121 min. and above | 0.587 | 0.305 | 8.516 | -22.287 | 39.320 | |||
| GFBS | < 0.001 | -0.341 | -0.505 | -0.623 | -0.387 | |||
| TUHS | 0.044 | 0.083 | 0.107 | 0.003 | 0.210 | |||
Dependent variable: BADLI
Discussion
This study aimed to examine the associations among perceived burdensomeness, technology use habits, and activities of daily living in older adults living in rural areas, making an initial empirical contribution to this underexplored field. The findings revealed that daily living activities among elderly individuals living in rural areas are significantly related to age, perceived burden, and technology use. In addition, perceived burdensomeness was negatively associated with ADL, whereas technology use was positively associated. Multiple regression analyses further supported these associations and indicated that perceived burdensomeness and technology use were significant predictors of activities of daily living.
This study found that the level of ADL dependence tended to increase with increasing age, whereas the use of digital tools was associated with lower levels of dependence. While there is extensive evidence in the literature that age correlates with greater functional dependence in older individuals [5, 34], a growing number of studies have also demonstrated that the use of digital tools is linked to reduced dependence [35–37]. The increase in functional dependence among older individuals with advancing age reflects natural declines in physical and cognitive capacity [38]. However, these findings indicate that higher levels of digital tool use are correlated with lower levels of dependency, suggesting that access to and use of technology may support older adults in maintaining independence by facilitating daily activities. Digital technologies can help improve the quality of life and functioning of older adults by providing convenience in health monitoring and communication. Particularly in rural areas, digital tools may help mitigate the effects of age-related loss of function by partially overcoming barriers to accessing health services and social support networks. Therefore, promoting digital literacy and technology use may still be beneficial for supporting independence in older adults.
The findings also show that perceived burdensomeness was negatively associated with activities of daily living among older adults living in rural areas, consistent with previous research [39, 40]. In rural areas, older adults who experience difficulties with ADL may not receive timely and adequate help due to limited access to health services and insufficient social support networks [41]. Consequently, the loss of independence and self-care skills can become a psychological stressor, reducing energy, motivation, and social interaction capacity, which can lead to isolation, increased depressive symptoms, and heightened perceived burdensomeness [42]. When individuals are unable to meet their own needs, they may perceive themselves as a burden to their families, which further reinforces these feelings. However, chronic health problems and income level may have indirect effects on older adults’ daily living activities through access to supportive resources and care options. Accordingly, efforts to support the functional independence of older adults living in rural areas and reduce their perceived burden should focus on strengthening access to preventive and curative health services and improving supportive environments, especially for those facing socioeconomic disadvantages.
Another important finding is that technology supports ADL among older adults in rural areas. The contribution of digital technologies to the daily functioning of older adults becomes even more evident in rural living conditions. Our data are consistent with previous studies [36, 37, 43]. reporting that the use of digital tools is related to lower risk of ADL dependence and better ADL performance. Digital tools may help older adults make daily plans, maintain social relationships, and engage more actively with their surroundings [43]. Previous research also shows that older adults who use digital technologies communicate more frequently with their social environment, visit family and friends more often, and participate more actively in community activities [36, 37]. These findings suggest that technological support contributes not only to individual independence but also to social integration through social participation. This highlights the potential of digital tools to help reduce the isolation caused by physical distance and limited access to services in rural areas. Enhancing older adults’ access to technology and their ability to use it can therefore be viewed as an important resource for supporting both functional independence and social connectedness. In conclusion, digital technologies should be regarded not only as facilitators but also as potential protective resources that support autonomy and social participation in later life.
Limitations
This study has some limitations. First, the accuracy and reliability of the responses may be limited because the data are based solely on older adults’ self-reports. In addition, since the study was limited to older adults living in rural areas, the findings may not generalize to older adults in urban or other geographical areas. Additionally, due to non-probabilistic sampling, the findings cannot be generalized to the broader population. Moreover, due to the cross-sectional design of the study, causal inferences cannot be drawn from the observed associations.
Conclusion
This study found that sociodemographic factors were significantly associated with perceived burdensomeness, activities of daily living, and technology use habits among older adults living in rural areas. Age and income were related to perceived burdensomeness, while age, perceived burdensomeness, and digital tool use were associated with activities of daily living. Education level was associated with technology use habits.
These findings highlight the importance of interventions that are sensitive to sociodemographic characteristics in supporting functional independence and reducing perceived burdensomeness among older adults. Expanding the use of digital tools and improving access to technology may contribute to older adults’ ability to perform daily activities, thereby supporting their independence. Additionally, targeted support programs for those facing economic or health-related challenges could help alleviate perceived burdensomeness.
In conclusion, multidimensional and comprehensive strategies may be beneficial for improving the quality of life of the rural elderly population. In this context, accessible, culturally sensitive educational programs that enhance digital literacy and facilitate technology adoption among rural older adults are recommended.
Authors’ contributions
Conceptualization: BB, EA. Data curation: BB, EA. Formal analysis: BB, EA. Funding acquisition: BB, EA. Investigation: BB, EA. Methodology: BB, EA. Project administration: BB. Resources: BB, EA. Software: BB, EA. Supervision: BB. Validation: BB, EA. Visualization: BB, EA. Writing – original draft: BB, EA. Writing – review and editing: BB, EA.
Funding
None.
Data availability
The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the principles outlined in the Declaration of Helsinki [21]. This study was approved by the Non-Interventional Clinical Research Ethics Committee of Bayburt University (Date: 25.12.2024, Number: 248968). The older adults who agreed to participate in this study were informed about the purpose of the study, the data collection procedure, the confidentiality of the data and their right to withdraw from the study without giving any reason. Verbal and written consent was then obtained prior to data collection and the recording of the interviews. Finally, the participants’ voice recordings were stored in an encrypted file accessible only to the study’s researchers. The data were not shared with anyone except the researchers of this study and were subsequently destroyed.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.