The digital technology–well-being paradox: No link between digital use and life satisfaction in older adults

Abstract

Introduction

The impact of digital technology use on older adults’ well-being remains uncertain. The objective of this study was to investigate the independent association between information and communication technology (ICT) use and life satisfaction in older adults, while accounting for socioeconomic, health, and social covariates.

Methods

We analyzed cross-sectional data from the 2025 DIGOLD survey of community-dwelling adults aged ≥60 years in Slovenia. Participants (N=983; mean age 72 years; 62% women) completed the Satisfaction With Life Scale and reported ICT use frequency (1=never to 5=daily). Covariates included age, gender, living arrangements, marital status, residence, education, household size, perceived economic situation, self-rated physical and mental health, and functional abilities. We computed Pearson correlations and fitted multiple linear regression models.

Results

ICT use correlated positively with life satisfaction in bivariate analyses (r=0.25, p<.001). In the full multivariable model (adjusted R²=0.32), ICT use was not a significant independent predictor (p>.05). Higher life satisfaction was associated with better mental health (β=0.252, p<.001), better financial situation (β=0.280, p<.001), better physical health (β=0.118, p=.001), and better functional abilities (β=0.085, p=.021); lower life satisfaction was associated with being unmarried (β=−0.081, p=.006) and institutional living (β=−0.115, p<.001).

Discussion

Among older Slovenians, the simple positive link between ICT use and life satisfaction was fully explained by socioeconomic and health advantages among frequent users. Digital engagement alone may not raise life satisfaction without concurrent gains in health, financial security, or social connection. Digital inclusion initiatives should emphasize meaningful, socially connective uses of technology and be paired with supports that address mental and physical health and economic well-being.

Keywords

life satisfaction older adults information and communication technology (ICT)digital technology use digital literacy

1. Introduction

The world’s population is ageing at an unprecedented pace. By 2030, globally one in six people will be aged 60 or older – around 1.4 billion, up from about 1 billion in 2020.¹ The number of individuals aged 80+ is projected to triple between 2020 and 2050, reaching approximately 426 million.¹ Such demographic shifts present far-reaching challenges for pension systems, healthcare, and social care infrastructures. In response, policymakers emphasize healthy and active ageing, including the promotion of digital inclusion for older adults, under the premise that bridging the “grey digital divide” can support older adults’ well-being.²

Life satisfaction – a person’s cognitive evaluation of their overall quality of life – is often highlighted as a key component of successful ageing. Higher life satisfaction in later life is linked to better mental and physical health, physical activity, lower depression risk, greater resilience, and even longevity.^3–5 Given its importance and modifiable nature, life satisfaction serves as both an outcome and target for interventions aimed at improving older adults’ quality of life.

Decades of gerontological research show that life satisfaction in older age is shaped by multiple demographic, socioeconomic, and health related factors. Some of the most consistently identified predictors include:

1. Health status: Perhaps the strongest determinant of late-life well-being is self-rated health (both physical and mental). Older individuals who perceive their health positively tend to report much higher life satisfaction.^6,7 Declines in health and functional ability are associated with drops in life satisfaction, whereas maintaining good health predicts greater satisfaction and even mediates longevity benefits.⁸ In our context, measures of physical health, mental health, and functional capacity are expected to show robust positive relationships with life satisfaction, as found in prior studies.⁹

2. Economic and educational factors: Socioeconomic status plays a significant role. Higher income and better perceived financial security correlate with greater life satisfaction in later life,^10,11 although the effect of income may plateau at higher levels.^10,12 Higher educational attainment is often associated with higher satisfaction, potentially via pathways like better health literacy, social status, and problem-solving skills.¹¹ However, education’s influence can be weaker than that of income for older adults. In short, having sufficient economic resources and financial stability is strongly predictive of well-being in old age.

3. Social and demographic factors: Social relationships and living arrangements are crucial. Being married or in a cohabiting partnership is generally linked to higher life satisfaction among older adults, relative to being widowed or single.¹⁰ The emotional and practical support from a spouse or partner can buffer stress and promote happiness, though relationship quality matters – a low-quality marriage may undermine well-being more than staying single.^10,13 Likewise, living with family or others, and having a larger household or strong social network, is associated with greater life satisfaction.¹⁴ Frequent, high-quality interactions with friends, family and community (e.g. volunteering, social groups) provide meaningful engagement and reduce loneliness, thereby improving satisfaction.¹⁵

4. Other factors: Show more nuanced effects: age itself has a complex relationship with life satisfaction. Cross-sectional surveys often find a U-shaped curve where midlife dips are followed by higher satisfaction in late life, but longitudinal analyses suggest more heterogeneous trajectories.^12,16 Gender differences in life satisfaction among older adults are typically small; any differences are often context-dependent and mediated by factors like social support or economic security.^17,18 In summary, healthier, financially secure older adults with supportive social ties and partnerships tend to report the highest life satisfaction, aligning with the broader literature on successful ageing.^19,20

In recent years, digital technology use has emerged as a potential factor influencing older adults’ well-being. information and communication technology (ICT) use might support life satisfaction in older age.^19,20 For example, a recent study found that internet use was associated with lower depression and improved psychological well-being among older adults.²¹ Similarly, smartphone use can enhance life satisfaction indirectly, through emotional and functional affordances such as social or health-related apps.²² Greater digital literacy and engagement (access to devices, competence in their use, and integrating technology into daily life) is posited to enhance autonomy and connectivity for older adults^15,23 There are several mechanisms by which ICT use might support life satisfaction in older age:

1. Enhanced Social Connectivity: Communication tools like email, messaging apps, video calls, and social media can help older adults maintain contact with family and friends, thereby mitigating loneliness and social isolation.^15,20,24 For example, using video chat to see grandchildren or participating in online communities could bolster an older adult’s sense of social inclusion and satisfaction.

2. Access to Services and Information: The internet enables easier access to health information, telehealth services, e-government portals, banking, and shopping from home.^20,24 This convenience can be particularly valuable for those with mobility limitations, potentially improving their quality of life by preserving independence.

3. Cognitive Engagement and Self-Efficacy: Online learning platforms, games, or other digital leisure activities can provide mental stimulation and a sense of achievement or competence.^23,25 Successfully adapting to new technologies may also boost older adults’ self-efficacy and confidence in navigating modern society.²⁰

Reflecting these theoretical benefits, empirical findings on ICT use and well-being have been promising in some contexts. For example, a longitudinal study in South Korea found that higher digital literacy (spanning access, skill, and usage) was consistently associated with greater life satisfaction among adults 65+ over a four-year period.²⁶ Another study reported that the positive effect of digital engagement on social well-being was mediated by social capital – implying that being online only improves life satisfaction if it helps older adults grow or maintain supportive social networks.⁶ In China, researchers observed that digital literacy among older adults led to healthier behaviors and enhanced happiness, especially in rural areas, by increasing future orientation and access to information.⁷ Such studies reinforce a narrative that closing the digital divide could empower older people and improve their quality of life.

Slovenia, like many European countries, has actively promoted digital inclusion of older citizens. National initiatives – for instance, the Simbioza Mobiln@ project – have provided digital skills training to thousands of older adults, yielding improvements in participants’ tech competencies and social inclusion. Slovenia’s Recovery and Resilience Plan similarly prioritizes digital literacy for all age groups as part of its modernization strategy.²⁷ Despite these efforts, there has been limited research in Slovenia examining whether increased ICT use actually translates into higher life satisfaction for older adults at the population level. Much of the evidence for benefits of digital engagement in later life comes from East Asia and North America, or from specific European countries like Italy or Spain, but few studies have focused on Slovenians aged 60+ while accounting for the full range of demographic, socioeconomic, and health influences.

To address this gap, the present study investigated the relationship between older Slovenians’ use of digital technology and their life satisfaction, controlling for key personal factors. We specifically aimed to¹: quantify the association between frequency of ICT use and life satisfaction in a sample of adults aged 60 or above; and² determine whether any such association persists after adjusting for potential confounders including age, gender, living arrangement, marital status, place of residence, education, household size, economic situation, health and functional abilities. We hypothesized that more frequent digital technology use would be positively associated with life satisfaction (H1), and that this relationship would remain significant even after controlling for the aforementioned factors (H2). In summary, our working expectation – in line with prevailing policy enthusiasm – was that digital engagement acts as a boon for older adults’ subjective well-being, above and beyond the contribution of established predictors.

2. Methods

2.1. Study design and participants

We analyzed data from the 2025 DIGOLD survey (Digitalisation in the (post)COVID-19 era and its effect on quality of life and social inclusion of older adults), a cross-sectional study of older adults in Slovenia. The target population was community-dwelling adults aged 60 years and above. Data were collected between January and March 2023. The final sample comprised N = 983 older adults (60+) recruited via a non-probability convenience sampling strategy. Participants were reached through older adults’ centers, community organizations, healthcare facilities, and online outreach, with an effort to include a diverse mix of urban, suburban, and rural residents. Inclusion criteria required that individuals be 60 or older, reside in Slovenia permanently, and be capable of providing informed consent. Those with severe cognitive impairments or other conditions precluding participation were excluded. All participants provided informed consent, and the study adhered to ethical standards (in line with the Declaration of Helsinki and national research ethics guidelines).

The study protocol was approved by the Ethics Committee for Research at Masaryk University (approval number: EKV-2022-007; approved January 9, 2023). The study was conducted in accordance with the Declaration of Helsinki and applicable national ethical guidelines. Given the survey-based nature of the study, the Ethics Committee waived the requirement for written informed consent; therefore, informed consent was obtained electronically from all participants.

2.2. Measures

2.2.1. Life satisfaction

The dependent variable was life satisfaction, measured using the Satisfaction With Life Scale (SWLS).⁴ The SWLS consists of 5 statements (e.g. “In most ways my life is close to ideal”) rated on a 7-point Likert scale from 1 (“strongly disagree”) to 7 (“strongly agree”). We used the standard sum score (range 5–35) with higher scores indicating greater life satisfaction. The SWLS is a well-established instrument with good reliability in older populations (Cronbach’s α > 0.80) and has been widely used in gerontological research. In the present study, the Cronbach’s α for the SWLS was .90.

2.2.2. Digital technology use

The key independent variable was the frequency of digital technology use. Participants reported how often they use various digital technologies – such as computers, tablets, smartphones, the internet in general, and digital services (e.g. e-banking, e-health, e-government, online shopping) – on a 5-point scale: 1 = “never” up to 5 = “daily”. For analysis, we aggregated these items to create an overall digital technology use frequency score, with higher values indicating more regular engagement with digital devices/services. The underlying assumption was that more frequent digital technology use reflects greater digital engagement or literacy. In the present study, the five-item ICT use measure demonstrated good internal consistency (Cronbach’s α = .87).

2.2.3. Control variables

Based on prior research and our hypotheses, we included a range of demographic, socioeconomic, and health-related controls to isolate the unique effect of digital technology use. These covariates were:

• Age: in years (continuous).

• Gender: coded 1 for female, 2 for male.

• Living arrangements: dichotomized as private home (independent or with family) versus institutional residence (e.g., nursing home).

• Marital status: categorized as married/in domestic partnership, widowed, single (never married), or divorced. For regression modeling, this was effects-coded or entered as dummy variables, with a focus on the contrast between those currently partnered vs. not.

• Place of residence: urban vs. rural locality.

• Education level: an ordinal variable ranging from incomplete primary education (lowest) up to postgraduate degree (highest).

• Household size: number of people in the household (as an indicator of social support at home).

• Self-rated current economic situation: the individual’s perceived financial situation or difficulty, treated as an indicator of income/wealth status (with higher values for better financial condition).

• Self-rated physical health: typically a single-item measure asking participants to rate their health (e.g. on a scale from poor to excellent).

• Self-rated mental health: a self-assessment of overall emotional/psychological well-being.

• Self-rated functional abilities: perception of one’s ability to perform daily activities independently (e.g. mobility, self-care).

All control variables were collected via the structured questionnaire, either through face-to-face interviews or an online survey (for those participants comfortable with that mode). Established single-item measures or categorical items were used for these self-reports, and descriptive statistics for all variables are presented in Table 1.

Table 1.

Descriptive statistics of used variables.

Discrete and ordinal variables	min	Max	Mean	sd
Age	60	94	72.20	8.10
Household size	1	9	2.48	1.54
Economic situation	1	5	3.49	0.78
Physical health	1	5	3.58	0.81
Mental health	1	5	3.89	0.79
Functional abilities	1	5	4.04	0.93
SWLS	1	7	4.94	1.24
Digital technology use	1	5	2.76	1.20
Nominal variables	n	%
Gender
Female	611	62.2
Male	372	37.8
Living arrangements
Home	892	90.7
Institution	91	9.3
Marital status
Married/domestic partnership	555	56.5
Widowed	308	31.3
Single	55	5.6
Divorced	65	6.6
Place of residence
Urban	466	47.4
Rural	517	52.6
Education
Not finish primary education	31	3.2
Primary education	124	12.6
Vocational education	259	26.4
Secondary education	279	28.4
Higher vocation education	137	13.9
bachelor’s degree	59	6.0
University degree	71	7.2
master’s degree	15	1.5
PhD degree	8	0.8

Note. N = 983; sd – standard deviation.

2.3. Statistical analysis

We first conducted descriptive analyses and bivariate correlations to examine the associations between life satisfaction and each predictor. Pearson correlation coefficients were calculated to see how digital technology use, age, health ratings, etc., relate to the SWLS scores. Next, to test our main hypotheses, we performed a multiple linear regression analysis with life satisfaction as the dependent variable. We entered digital technology use and all control variables into the regression model to assess their simultaneous effects. This allowed us to evaluate whether the frequency of digital technology use uniquely predicts life satisfaction, when accounting for demographics, socioeconomic status, and health-related factors. Multiple linear regression analyses were conducted using the Enter method, whereby all theoretically relevant predictors were included simultaneously in the model. In addition, exploratory moderation analyses were conducted using hierarchical regression to examine whether age group or gender moderates the association between digital technology use and life satisfaction. Interaction terms were entered in a second block after inclusion of all main effects. The threshold for statistical significance was set at p < .05 (two-tailed). All analyses were conducted using standard statistical software, and model assumptions (normality, homoscedasticity, multicollinearity) were checked to ensure validity of the regression results.

3. Results

3.1. Sample characteristics

The analyzed sample (N=983, aged 60–98) was 54% female, with a mean age of around 72 years (SD ≈ 8 years). About 62% of participants were married or cohabiting, 25% were widowed, and the rest single or divorced. The majority lived in private homes (over 95%), with only a small fraction residing in institutions. Educational attainment ranged widely; roughly 20% had tertiary education (college or higher). In terms of digital engagement, approximately 40% of the sample reported using some form of digital technology daily, while about 25% used digital technology rarely or never – highlighting the variability in digital inclusion even within this group of older Slovenians. Average life satisfaction (SWLS) in the sample was moderate to high (mean ∼ 26 on the 5–35 scale, indicating generally positive life evaluations). Descriptive statistics for all variables, including control variables, are presented in Table 1.

3.2. Bivariate associations between life satisfaction and control variables

Table 2 (correlation matrix) confirmed many expected relationships. In simple correlations, life satisfaction was significantly higher among those with better health, more economic security, and stronger social ties. For example, self-rated physical health (r ≈ 0.409, p < .001) and mental health (r ≈ 0.452, p < .001) had some of the strongest positive correlations with life satisfaction. These findings are consistent with Puvill et al.⁹ who found that poor physical performance, low functional status, depressive symptoms and perceived loneliness were strongly related to lower life satisfaction (both p < 0.001).

Table 2.

Correlation matrix (independent and control variables with internet usage) with Pearson’s correlation coefficient r.

	1	2	3	4	5	6	7	8	9	10	11	12	13
1	1	.252!	-.111!	-.004	-.239!	-.141!	.007	.171!	.084!	.409!	.409!	.452!	.379!
2	.252!	1	-.483!	.002	-.302!	-.102!	-.140!	.497!	-.045	.320!	.350!	.295!	.329!
3	-.111!	-.483!	1	-.015	.234!	.137!	-.027	-.171!	-.048	-.055	-.270!	-.146!	-.359!
4	-.004	.002	-.015	1	-.009	.171!	-.026	-.034	-.114!	-.098!	-.014	.002	.024
5	-.239!	-.302!	.234!	-.009	1	.177!	-.064*	-.104!	-.133!	-.088!	-.174!	-.176!	-.310!
6	-.141!	-.102!	.137!	.171!	.177!	1	-.131!	.003	-.278!	-.083!	-.069*	-.018	-.094!
7	.007	-.140!	-.027	-.026	-.064*	-.131!	1	-.184!	.162!	-.065*	-.031	-.017	.027
8	.171!	.497!	-.171!	-.034	-.104!	.003	-.184!	1	-.128!	.388!	.243!	.178!	.220!
9	.084!	-.045	-.048	-.114!	-.133!	-.278!	.162!	-.128!	1	0.04	.002	.002	.011
10	.409!	.320!	-.055	-.098!	-.088!	-.083!	-.065*	.388!	.040	1	.344!	.293!	.244!
11	.409!	.350!	-.270!	-.014	-.174!	-.069*	-.031	.243!	.002	.344!	1	.530!	.599!
12	.452!	.295!	-.146!	.002	-.176!	-.018	-.017	.178!	.002	.293!	.530!	1	.503!
13	.379!	.329!	-.359!	.024	-.310!	-.094!	.027	.220!	.011	.244!	.599!	.503!	1

Note. ! - Correlation is significant at the 0.01 level (2-tailed); * - Correlation is significant at the 0.05 level (2-tailed).

1=SWLS – Satisfaction with life scale; 2=DTU – Digital technology use; 3=AGE – Age; 4=Gen – Gender; 5=LIVA – Living arrangements; 6=MAR – Martial status; 7=RES – Place of residence; 8=EDU – Education; 9=HHSIZE – Household size; 10=ECON – Economic situation; 11=PH –Physical health; 12=MH – Mental health; 13=FUNC – Functional abilities.

Participants who rated their financial situation more positively were also significantly more satisfied with life - consistent with findings by Fang et al.,²⁸ where financial well-being strongly and positively predicted life satisfaction (β = 0.34, p < 0.01).

Additionally, living with others - evinced by larger family network size - and being married or partnered were both positively associated with higher life satisfaction. SHARE and ESS data reveal that stronger household or social connections bolster well-being, while the absence of a partner (e.g., due to widowhood or divorce) is one of the most potent risk factors for decreased life satisfaction in older Europeans.²⁹

Living in one’s own home - rather than in a nursing home - was negatively linked with institutional residence in terms of well-being. Older adults residing in community settings tend to exhibit higher life satisfaction, with this effect mediated by greater social support and sense of meaning in life.^30,31

Education level showed a positive correlation with life satisfaction, consistent with broader findings that higher educational attainment is linked to greater subjective well-being.³² Likewise, we observed a slight negative correlation between age and life satisfaction, echoing observations in some subgroups of older adults, though literature reports mixed patterns, including U-shaped trends across the lifespan.³³

In line with prior findings, our bivariate analyses showed a strong positive association between digital technology use and life satisfaction (r ≈ 0.252, p < .001). Similar patterns have been observed in other contexts - for example, Lee³¹ found that digital literacy consistently correlated with higher life satisfaction among older Korean adults, and Cheng et al.³⁴ observed that internet use related positively to life satisfaction in Chinese elders, notably through increased offline social interaction.

In other words, older adults who used digital technology more frequently tended to report higher life satisfaction without accounting for other factors. This simple relationship aligns with the notion that digitally active older adults might be happier, on average, than those who are digitally disengaged – but it does not account for underlying differences (for instance, those using digital technology more could also be healthier or wealthier).

3.3. Multivariate regression: Significant predictors of life satisfaction

A multiple linear regression analysis was conducted to examine predictors of life satisfaction (Table 3). The overall model was statistically significant, F (11, 890) = 41.09, p < .001, explaining 33% of the variance in life satisfaction (R² = .337, Adjusted R² = .329). The corresponding effect size (f² = .51) indicates a large overall effect according to Cohen’s criteria. Multicollinearity diagnostics indicated acceptable tolerance levels; variance inflation factors ranged from 1.16 to 6.35, suggesting no evidence of problematic multicollinearity.

Table 3.

Predictors of life satisfaction: Multiple linear regression model.

	B	Std. Error	Beta	t-test	p	95% CI (lower, upper)
Gender	0.193	0.174	0.076	1.112	0.266	-0.52	2.11
Age	0.006	0.007	0.037	0.815	0.415	-0.15	0.53
Education	-0.041	0.026	-0.051	-1.553	0.121	-0.01	0.02
Economic situation	0.445	0.050	0.280	8.857	<.001	-0.09	0.01
Physical health	0.180	0.056	0.118	3.193	0.001	0.35	0.54
Mental health	0.395	0.053	0.252	7.439	<.001	0.07	0.29
Marital status	-0.117	0.042	-0.081	-2.763	0.006	0.29	0.50
Functional abilities	0.113	0.049	0.085	2.312	0.021	-0.20	-0.03
Household size	0.035	0.024	0.043	1.487	0.137	0.02	0.21
Place of residence	-0.502	0.131	-0.115	-3.838	<.001	-0.01	0.08
Digital Technology Use (DTU)	0.008	0.067	0.008	0.117	0.907	-0.76	-0.25
DTUxGender	-0.003	0.006	-0.042	-0.605	0.546	-0.12	0.14
DTUxAge	0.002	0.008	0.013	0.227	0.821	-0.01	0.01

Note. R² = .34; Adjusted R² = .33; F(11, 890) = 41.091, p < .001.

Note. B = Unstandardized Coefficients; Std. Error – Standard Error; β - Standardized Coefficients; t: t-test; p –probability; 95% CI (lower, upper) - 95% confidence intervals (CIs).

Exploratory moderation analyses were conducted using hierarchical regression to assess whether age group or gender moderates the association between digital technology use and life satisfaction. Interaction terms were entered in a second block after inclusion of all main effects. Neither the digital technology use × age group interaction nor the digital technology use × gender interaction was statistically significant (both p > .05), and inclusion of the interaction terms did not significantly increase explained variance (ΔR² < .01). These findings suggest that the null association between digital technology use and life satisfaction is consistent across these demographic subgroups.

Consistent with theoretical expectations and the bivariate patterns, several control variables emerged as significant predictors of life satisfaction, whereas digital technology use did not. Better perceived financial situation (β = .280, p < .001), better mental health (β = .252, p < .001), better physical health (β = .118, p = .001), and better functional ability (β = .085, p = .021) were significant positive predictors of life satisfaction. Being unmarried (β = −.081, p = .006) and living in an institutional setting (β = −.115, p < .001) were associated with lower life satisfaction (see Table 3).

• Perceived financial situation: Our analysis indicates that perceived financial situation was a robust positive predictor of life satisfaction (β ≈ +0.264, p < .0001), indicating that individuals who feel financially secure tend to report higher life satisfaction when controlling for other factors. This finding aligns with broader evidence: a meta-analysis reports a consistent, moderate positive relationship between financial satisfaction and overall subjective well-being (pooled r ≈ 0.41, p < .001),³⁵ and neurobehavioral research indicates that perceived financial security enhances everyday happiness via anticipatory emotional processes.³⁶

• Perceived Health Status: Self-reported mental health emerged as one of the most robust predictors of life satisfaction, even after adjusting for income, general health, and other covariates. Evidence from large-scale population studies supports this pattern. For example, an analysis of more than 400,000 respondents from the Canadian Community Health Survey (2003–2012) showed that individuals reporting “fair” mental health had 2.35 times greater odds (95% CI 2.21–2.50) of reporting higher life satisfaction compared to those rating their mental health as “poor”.³⁷

In our analysis, physical health was also a significant positive predictor of life satisfaction (β ≈ 0.111, p = .002), consistent with broader empirical findings. Lau et al.,³⁸ for instance, reported a strong association between better self-rated general health and life satisfaction (Goodman and Kruskal’s γ = .514, p < .001).³⁸ Similarly, Bramhankar et al.³⁹ found that older adults with good self-rated health were more than four times as likely (OR = 4.45, p < .001) to report high life satisfaction. Taken together, these findings reinforce the central role of perceived health - particularly mental health - in shaping subjective well-being in later life.

• Socio-demographic Factors: Marital status and living arrangements showed significant effects in the model. Being currently unmarried (which in this sample largely meant single, widowed, or divorced), was associated with lower life satisfaction compared to being married/cohabiting (β ≈ –0.077, p = .005), consistent with findings by Fernandez-Portero et al..⁴⁰ Similarly, living in an institutional setting (or alone) versus living with family had a negative association (β ≈ –0.117, p = .002). These results align with the idea that social connection – through a spouse or household members – is beneficial for life satisfaction, whereas living alone or in a nursing home can pose risks to subjective well-being. Other demographic controls (age, gender, education, etc.) did not show statistically significant unique effects in the final model once health and economic variables were accounted for, which is not unusual given the inter-correlations among these factors.

• Digital Technology Use: Contrary to our hypothesis, the frequency of digital technology use did not emerge as a significant independent predictor of life satisfaction in the multivariate analysis. In other words, after accounting for age, gender, socioeconomic status, health, and social factors, higher digital technology use was not associated with higher (or lower) life satisfaction – its unique contribution was essentially zero. This result was unexpected, as we had anticipated a positive effect based on prior research. The initial positive correlation between digital technology use and life satisfaction appears to have been fully explained by confounding variables. Indeed, participants who were frequent digital technology users tended to be younger, more educated, in better health, and more financially comfortable — supporting findings such as those from Visaria et al.,⁴¹ who report that age, education, and income are positively associated with digital usage frequency among older adults. Once those advantages were controlled, simply using digital devices more often did not boost life satisfaction. It is important to note that no evidence of a negative effect was found either – it was a null effect.

In summary, our key finding is that while core factors like health and financial security show strong relationships with life satisfaction (as expected), digital technology use on its own was not a significant contributor to how satisfied these older adults were with their lives, once all other factors were taken into account. This finding runs counter to the optimistic hypothesis that encouraging older adults to be more digitally active will directly enhance their well-being.

4. Discussion

This study examined the relationship between digital technology use and life satisfaction in a sample of nearly one thousand older adults in Slovenia. The findings indicate that frequent digital technology use is not independently associated with higher life satisfaction after controlling for demographic, socioeconomic, and health-related factors. Thus, our data did not support the hypothesis that greater digital engagement directly enhances life satisfaction (H1 and H2 were not confirmed).

Furthermore, the absence of significant moderation effects suggests that digital technology use does not differentially benefit specific demographic subgroups within this population. In other words, the null association was consistent across age groups and between men and women. This finding challenges assumptions that digital engagement may be particularly protective for the oldest-old or for socially vulnerable groups.

Although this outcome contrasts with some prior evidence from other regions and with policy narratives emphasizing digital inclusion as a pathway to improved well-being, our results highlight the continued predominance of traditional determinants of life satisfaction. In particular, mental and physical health, financial well-being, and marital status emerged as the most influential predictors. Taken together, these findings suggest that subjective life satisfaction in later life remains primarily rooted in health and socioeconomic conditions, with digital technology engagement playing, at most, a secondary or indirect role.

Several explanations may account for the absence of a significant association between digital technology use and life satisfaction. First, digital engagement may reflect underlying advantages rather than independently enhance well-being. Older adults who use digital technologies more frequently are often healthier, better educated, and more affluent,^42,43 patterns also observed in our sample. Once these structural advantages are controlled, the additional contribution of digital use appears minimal. This interpretation aligns with models emphasizing that social support and motivation are central to the translation of technology use into well-being outcomes; for example, Ma⁴⁴ demonstrated the importance of partner support in facilitating beneficial digital engagement. Individuals facing serious health or financial challenges⁴⁵ are unlikely to improve life satisfaction solely through increased online activity, as fundamental well-being drivers must be met first.⁴⁶

Second, digital technology use is heterogeneous in purpose and quality. ICT engagement may be social, instrumental, or informational,⁴⁷ yet our measure captured only frequency, not context or meaning. Prior research suggests that benefits depend on specific activities and individual circumstances. For example, communication-oriented uses such as messaging or video calls have been linked to higher life satisfaction in certain subgroups,²³ whereas other activities show limited or no benefit.⁴⁸ Methodological limitations in measuring digital engagement have also been noted.⁴⁹ Qualitative evidence further suggests that perceived well-being gains may stem more from supportive learning environments than from technology itself.⁵⁰ Thus, frequency-based indicators may dilute meaningful effects by aggregating qualitatively distinct experiences.

Third, digital engagement may exhibit threshold or saturation effects. A basic level of digital access may support social inclusion—particularly evident during the COVID-19 period⁵¹—but additional use may yield diminishing returns once core social and informational needs are met. In a context such as Slovenia, where many older adults report relatively high life satisfaction and strong family networks, increased digital use may not substantially enhance subjective well-being. For those experiencing profound health or social difficulties, digital tools alone may be insufficient to offset structural disadvantages.

Finally, time-use trade-offs may also play a role. Increased digital engagement can potentially displace other activities,⁴⁹ and some studies report mixed or even negative associations between intensive internet use and happiness.⁵² Although digital communication can maintain social ties, it may not fully substitute in-person interaction, and excessive passive consumption may expose individuals to stressors. At the same time, longitudinal evidence indicates that computer use can be associated with better self-reported health and functioning.^7,52,53 In our data, digital technology use was neither harmful nor beneficial, suggesting that moderate use is likely neutral with respect to overall life satisfaction.⁵⁴

Our finding of no significant independent effect of digital technology use on life satisfaction contrasts with several studies from East Asia that motivated our hypotheses.⁷ For example, Lee²⁶ in South Korea found a positive association, and similarly positive results have been reported in some Chinese older populations.^7,20 These differences could be due to contextual factors – perhaps older adults in those settings use technology in more socially engaging or empowering ways (such as popular social media platforms in Asia, or community forums) compared to our Slovenian sample. Cultural differences in the acceptance of digital technology and the opportunities it offers might modulate its impact on well-being. It’s also worth noting that Europe-focused studies have shown mixed results, aligning more with what we observed. A recent large-scale analysis of Europeans (ESS data) pointed out that empirical evidence on digital technology’s benefits for well-being is inconclusive^53,55. Programs under the EU Digital Decade strategy—and national efforts like Slovenia’s Recovery and Resilience Plan (RRP)—are rolling out training and access initiatives intended to enhance older adults’ social connectedness, access to services, and overall well-being.^56,57 Our results are a reminder that access and usage alone are not a panacea. Simply getting more older adults to use the internet or smartphones, without considering how they use them and what underlying needs they have, may not translate into higher life satisfaction. This doesn’t mean such programs lack value – digital skills can indeed help older adults maintain independence and reduce isolation, as seen in initiatives like Simbioza which reported improved social inclusion.²⁷ However, to truly impact subjective well-being, digital inclusion efforts might need to focus on meaningful use and integration. This could involve: tailoring training content so that older adults learn to use digital technology in ways that directly enrich their lives (e.g. communicating with loved ones, joining interest-based communities, telehealth for better managing health), ensuring accessible and age-friendly technology design to minimize frustration, and addressing issues like online safety and misinformation that can adversely affect older adults’ trust and mental state. The European Fundamental Rights Agency notes that many older adults face significant barriers when accessing digitized public services—such as limited digital skills, costly technology, and insufficient policy support—and stresses the need for supportive measures to ensure that they can truly benefit from online platforms.⁵⁸ Furthermore, although the EU has set ambitious targets (such as having at least 80% of adults with basic digital skills by 2030), currently only about one-third of older Europeans (55–74) have at least basic digital skills,⁵⁹ compared to over 80% of young adults. Bridging this gap is important, but our study suggests that we must also bridge the gap between digital use and positive outcomes. Policymakers should be mindful that digital technology is a tool, not an end in itself – its benefits for well-being will depend on the social and human context in which it’s used. Initiatives such as intergenerational digital technology training (e.g., pairing older adults with young tutors) or creating senior-friendly online communities can amplify positive outcomes by blending technology use with social support. For example, a digital literacy program pairing undergraduates with low-income older adults demonstrated not only improved digital skills and confidence but also the formation of emotionally positive trainer-trainee bonds.⁶⁰ Similarly, a realist review of 27 intergenerational digital programs found that strategies—such as video calls with students or families—were effective in reducing loneliness and fostering well-being among older adults.⁶¹ In essence, the push for a digital Europe needs to go hand in hand with efforts to maintain and improve the health, social connectivity, and financial security of older adults. Our findings reinforce that improving those foundational aspects will likely yield the greatest gains in life satisfaction, with digital tools serving as a complementary support rather than a standalone solution.

This study contributes to the literature by providing data on the digital engagement–well-being link in a Central European context where such data have been scarce. Slovenia’s older population had not been extensively studied in this regard, and our research offers insights that complement findings from larger countries. The study controlled for a comprehensive set of variables, allowing a rigorous test of the unique impact of digital technology use on life satisfaction. The null finding is valuable for theory – it challenges assumptions of a universally positive digital effect and suggests researchers should examine mediators (like how and for what purpose technology is used) and contextual moderators (such as cultural attitudes or community support) more closely. From a practical standpoint, our results encourage a nuanced approach to digital inclusion programs, as discussed above. We also add to the growing evidence base within Europe that the relationship between digital technology use and older adults’ well-being is not straightforward and may require more targeted inquiry (for example, looking at specific outcomes like loneliness reduction or cognitive maintenance, rather than broad life satisfaction).

We acknowledge several limitations of our study. First, the design was cross-sectional, which precludes establishing causality or directionality. It is possible, for example, that individuals with higher life satisfaction are more inclined to engage in digital technology, rather than digital technology use influencing life satisfaction. Longitudinal research would be valuable in determining whether changes in digital engagement precede changes in well-being over time. Second, our measurement of digital technology use relied on an overall frequency indicator and therefore did not capture qualitative aspects of engagement, such as purpose, intensity, digital skills, or the subjective meaning attached to technology use. We did not differentiate between types of online activities (e.g., social communication, information seeking, entertainment) or the quality and context of those experiences. As a result, the measure may reflect exposure or access rather than meaningful digital engagement. If well-being benefits depend on the nature or quality of technology use rather than frequency alone, this limitation may have attenuated potential associations. Future studies should employ multidimensional measures of digital engagement and consider qualitative approaches to better understand how older adults integrate technology into their daily lives. Third, although the sample was sizeable, it was a convenience sample and not fully representative of the entire Slovenian older population. Participants may have been somewhat more active or healthier than the general older population, potentially limiting generalizability. In addition, very frail or institutionalized older adults were underrepresented, and the findings may therefore not extend to the oldest-old residing in care facilities, where digital technology use is typically lower. Fourth, the data were based on self-reported measures, which are subject to biases such as social desirability, inaccurate recall, or subjective interpretation. These biases may have influenced responses regarding digital technology use, life satisfaction, and control variables, potentially affecting the observed associations. Finally, although we included a broad range of demographic, socioeconomic, and health-related controls, unmeasured factors (e.g., personality traits such as openness to experience, cognitive functioning, or the quality of close relationships) may influence both digital technology use and life satisfaction. Such confounding variables could obscure small true effects or help explain why certain individuals may derive greater benefit from digital engagement than others.

Despite the null effect on life satisfaction, digital technology may still hold other benefits for older adults – for example, cognitive stimulation, convenience in daily tasks, or maintaining autonomy (being able to do things without help). Future research could explore those specific outcomes (beyond global life satisfaction) to see where digital engagement makes the biggest difference. Also, as digital tools evolve (e.g. user-friendly apps for older adults, telehealth expansion, virtual social groups), the impact on well-being might grow. Continuous monitoring of cohorts as they adopt newer digital technologies (like smart home devices or AI-based applications) will be important. Given the nuance revealed in recent studies, it would be fruitful to examine for whom and under what conditions digital technology use enhances well-being. The Italian study suggests gender and living arrangement matter²³; other factors might include baseline social support (do those living alone gain more from online connection?) or digital skill level (do more proficient users derive more benefit?). Understanding these nuances can help target interventions to the right groups – for example, encouraging specific online activities for those most likely to benefit (such as video calling for older men living alone), as Furlan and Meggiolaro²³ found. Finally, qualitative insights from older adults about their digital experiences could shed light on why some feel it improves their quality of life while others do not. Such insights can guide more empathetic and effective digital inclusion strategies.

5. Conclusion

This study found no significant independent relationship between digital technology use and life satisfaction among older adults in Slovenia after accounting for demographic, socioeconomic, and health-related factors. These findings suggest that simply increasing the frequency of internet or digital device use is not, by itself, a reliable pathway to improving subjective well-being in later life. Rather, life satisfaction continues to be most strongly shaped by established determinants such as physical and mental health, financial security, and social integration.

These results carry important implications for policy. Digital inclusion strategies should move beyond access and usage metrics and prioritize meaningful, purposeful engagement. Policymakers should ensure that digital initiatives for older adults are designed to enhance social connection, support health management, and promote autonomy in daily life, rather than focusing solely on increasing digital uptake rates. Training programs should be tailored to real-life needs and interests of older adults, with particular emphasis on communication-oriented and socially connective uses of technology.

Policymakers should also prioritize age-friendly design, accessible digital services, intergenerational training models, and ongoing human support to ensure that digital tools are usable, trustworthy, and aligned with older adults’ capabilities. Monitoring and evaluation efforts should assess not only digital access and skill acquisition but also tangible well-being outcomes.

Furthermore, digital inclusion policies should be embedded within broader social and health-support frameworks. Investments in digital upskilling are unlikely to substantially improve life satisfaction if underlying determinants such as poor health, social isolation, or financial insecurity remain unaddressed. Digital technology should therefore be conceptualized as a complementary tool that reinforces foundational well-being drivers, not as a standalone solution.

Ultimately, while bridging the digital divide remains important, our findings suggest that “more connection” does not automatically translate into “more satisfaction.” A nuanced and integrated approach—combining digital inclusion with health, social, and economic support policies—is likely to yield more meaningful improvements in the quality of life of ageing populations.

Footnotes

Acknowledgments

The authors thank all survey participants for their invaluable contributions to this research infrastructure.

ORCID iD

Voyko Kavcic

Ethical considerations

Data collection in DIGOLD has ensured compliance with legal norms and international ethical standards, including the Respect Code of Practice for Socio-Economic Research and the Declaration of Helsinki.

The study protocol was approved by the Ethics Committee for Research at Masaryk University (approval number: EKV-2022-007; approved on January 9, 2023). The study was conducted in accordance with the Declaration of Helsinki and relevant national ethical guidelines.

Given the survey-based nature of the study, the requirement for written informed consent was waived by the Ethics Committee. All participants provided informed consent prior to participation: verbal consent was obtained for in-person data collection by trained interviewers, and electronic consent was obtained for online participation.

The data used in this study were collected as part of the DIGOLD project (“Digitalisation in the (post)COVID-19 era and its effect on quality of life and social inclusion of older adults”). The authors are members of the DIGOLD research consortium and have full authorization to access and use the data for scientific analysis and publication in accordance with project governance and the approved ethical protocol.

Author contributions

Conceptualization: BG, VK, JG, PS; Data curation: BG, PS; Formal analysis: VK; Funding acquisition: VK; Investigation: BG; Methodology: BG, VK, PS, JG; Project administration: PS, BG; Resources: VK; Supervision: VK; Validation: VK, PS, JG; Visualization: BG; Writing – original draft: PS, BG; Writing – review & editing: VK, JG.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work on this paper is part of the project “Effect of digitalisation in (post)COVID-19 era on quality of life, and social inclusion of older adults” (DIGOLD), which is funded by the Czech Science Foundation (GAČR), project No. 22-05059L and by the Slovenian Research and Innovation Agency (ARIS), project No. J5-4580.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.*

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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