Attitudes related to technology for active and healthy aging in a national multigenerational survey

Rapid technological development promises new ways to support active and healthy aging, defined as optimizing opportunities for health, participation and security to enhance quality of life as people age. Despite widespread integration of diverse technologies in daily life, research has focused mainly on ICT, e-health, wearables, monitoring systems and smart homes, overlooking everyday technologies such as household equipment and vehicles. Evidence on how technology supports active and healthy aging is limited and sometimes conflicting, and larger quantitative studies are needed to understand determinants of acceptance and adoption. The unified theory of acceptance and use of technology (UTAUT) and UTAUT2 highlight user characteristics (age, sex, experience), intention to use (performance and effort expectancy, social influence), usage behavior (facilitating conditions), hedonic motivation, price/value and habit as determinants of adoption, but these models primarily address users’ adoption rather than adapting technology to users’ needs. Few studies use longitudinal or generational designs to distinguish age, cohort and period effects, and differences across generations remain underexplored. The GenerationTech project was designed to address these gaps using an interdisciplinary approach. This survey study aimed to describe attitudes and adoption related to technology in general and as a means to support active and healthy aging across three generations (30–39, 50–59, 70–79 years), guided by research questions about: (1) attitudes toward different types of technology and generational differences; (2) which products are perceived most important for active and healthy aging and generational differences; and (3) which product characteristics are important for adopting/accepting new technology across generations.

Prior work often characterizes older adults as technophobic or lower in computer self-efficacy, yet a meta-analysis indicates age is negatively related to acceptance mainly for technologies with unclear perceived functionality for older adults (e.g., social media). Perceived value, usefulness and ease of use are key facilitators of acceptance among older adults. Systematic reviews have examined acceptance of technologies for aging in place, smart homes, and e-health, but research frequently neglects everyday technologies (e.g., kitchen appliances, household equipment, cars). Evidence on technology’s capability to support active and healthy aging remains limited and mixed, and higher quality, larger quantitative studies are needed. UTAUT/UTAUT2 frameworks reliably explain adoption via user characteristics, expectancies, social influence, facilitating conditions, hedonic motivation, price/value and habit, but they emphasize user adoption rather than adapting technology to users’ needs. Period and cohort effects ("technology generations") may shape attitudes and adoption as different generations experience different technological developments across the life course.

Design: Quantitative, cross-sectional national survey within the 3-year GenerationTech project at Lund University, Sweden; approved by the Swedish Ethical Review Authority (ref. 2019-02072). Sampling and recruitment: A random sample of 10,000 addresses was drawn in August 2019 from the Swedish State Personal Address Register (SPAR), stratified by three age cohorts (30–39, 50–59, 70–79 years) and sex. Anticipating lower response rates in younger cohorts, different numbers of addresses were drawn per cohort. The targeted sample size was 3,598 (95% confidence level, 4% margin of error). Kantar Sifo conducted recruitment via mailed invitations (with consent information and unique survey logins), postal reminders, and up to eight follow-up phone attempts offering telephone interview or postal survey options. Data collection: GenerationTech questionnaire based on qualitative findings and relevant literature included 24 questions on attitudes and acceptance of a broad range of technologies used in everyday activities (e.g., household devices, kitchenware, cars, lightbulbs, TVs), ICT (smartphones, tablets, computers), welfare technologies (safety alarms, video surveillance, e-health), medical technologies and assistive technologies (walkers, wheelchairs, communication aids, medical products like pacemakers/insulin pumps). Seven questions covered demographics and self-rated general health, life satisfaction, and finances for technology needs. Estimated completion time 10–15 minutes. A pilot with 21 participants from the Kantar Sifo web panel informed minor revisions. Quality control included ongoing data checks, researcher monitoring after 10% completion, and listening to 5% of phone interviews. Sample: Final n=2,121 respondents (51% men, 49% women): 30–39 years n=639 (49% men/51% women); 50–59 years n=703 (49% men/51% women); 70–79 years n=779 (54% men/46% women). Mode: 95% online, 4% phone, 1% mail. Analytic approach: Descriptive statistics characterized the sample. Between-generation differences in product and characteristic preferences were assessed with chi-squared tests (two-sided; alpha P<0.05; Bonferroni correction). Binary logistic regression examined differences between generations in attitudes toward household devices (Table 3) and ICT products (Table 4), providing unadjusted and adjusted models. The dependent variables were attitude items (e.g., useful, user-friendly, time-saving, etc.); the independent variable was generation. Adjusted models controlled for country of birth, education, municipality size, self-rated economy, life satisfaction and general health. Analyses were performed in SPSS v27. No data were excluded.

Sample profile (Table 2): Among 2,121 respondents, most were born in Sweden and had at least compulsory education. The youngest cohort had higher education levels and better self-rated health; the oldest cohort rated economy and general health lower. Technology preferences for active and healthy aging (Fig. 1; Extended Data Table 1): Across cohorts, respondents preferred traditional technologies such as household devices, home entertainment, exercise devices and assistive devices. The oldest cohort (70–79) was significantly less interested than younger cohorts in activity sensors, exercise devices, personal health sensors, medical technologies, smart homes, welfare technologies, home/social robots, Internet-based shopping and services (multiple P<0.05 after Bonferroni). The youngest cohort (30–39) was significantly less interested than the oldest in household devices, home entertainment, motorized vehicles and social media. The middle-aged cohort (50–59) was more interested than the oldest in assistive devices, personal emergency response systems (PERS), and social media. Reasons for use (Fig. 2; Extended Data Table 2): Primary reasons were independence, contact with family/friends, physical activity, and being able to notify someone in case of fall/illness. Compared to younger cohorts, the oldest were significantly less interested in using technologies for time-saving, feeling safe, monitoring health, controlling home entertainment, accessing services, pleasure/entertainment, and shopping. The middle-aged cohort was less interested than the youngest in technology for time-saving. Attitudes toward household devices (Table 3): Relative to 30–39 years (reference), 70–79 years had lower odds of perceiving household devices as useful (OR 0.58, 95% CI 0.46–0.74), user-friendly (OR 0.61, 0.48–0.77), and time-saving (unadjusted OR 0.80, 0.64–1.00; adjusted 0.96, 0.75–1.24), but higher odds of stating they make them independent (OR 1.85, 1.39–2.64). The 50–59 cohort had lower odds of perceiving household devices as user-friendly (OR 0.78, 0.62–0.98) and higher odds for time-saving (OR 1.33, 1.03–1.72) and independence (OR 1.62, 1.22–2.14). Attitudes toward ICT products (Table 4): Relative to 30–39 years, 70–79 years had lower odds of perceiving ICT as useful (OR 0.54, 0.42–0.70), user-friendly (OR 0.33, 0.25–0.43), practical (OR 0.46, 0.36–0.59), and time-saving (OR 0.73, 0.57–0.93). The 50–59 cohort had lower odds for useful (OR 0.72, 0.56–0.93) and user-friendly (OR 0.52, 0.41–0.66) and practical (unadjusted OR 0.76, 0.60–0.95; adjusted 0.81, 0.63–1.03). Middle-aged respondents had higher odds of stating ICT made them independent (OR 1.45, 1.13–1.85); the oldest also showed higher adjusted odds for independence (OR 1.39, 1.08–1.79). Important factors for choosing/adopting technology (Extended Data Tables 3–4): Price, flexible use, and preference for standard over extra functions were important across cohorts; the oldest particularly emphasized environmental sustainability. Most respondents reported learning new products easily and keeping up with technology, though these were strongest in the youngest cohort. Additional descriptive insight: In the oldest cohort, 38% reported needing help with digital technologies, 7% had no Internet knowledge, and 17% did not use the Internet.

The study reveals both shared and distinct generational patterns in attitudes toward technologies supporting active and healthy aging. Across cohorts, respondents preferred traditional technologies (household and assistive devices) over newer options such as smart homes and welfare technologies, likely reflecting familiarity and perceived value as posited by UTAUT/UTAUT2. Despite stereotypes, many attitudes were shared across generations and most respondents felt able to keep up with technology, suggesting that age disparities may be diminishing with broader exposure and access. Generational differences were evident: the oldest cohort expressed less interest in digital and AI-driven solutions, health/activity monitoring, and Internet-based services/shopping, and weighed environmental sustainability more when adopting new technologies. The youngest cohort was least likely to view social media and some traditional technologies as means for active and healthy aging, whereas the middle-aged cohort was most likely to include social media and showed higher perceived independence from ICT. Differences likely reflect period and cohort effects (“technology generations”) and domestication processes whereby technology is integrated into everyday life over time. Findings suggest that mismatches between technology design and older adults’ needs (e.g., complexity, limited perceived usefulness) rather than age per se impede adoption, underscoring the need for inclusive design and earlier, sustained user involvement across generations.

Applying a generational perspective, this national survey identified both similarities and differences in attitudes toward and acceptance of technologies for active and healthy aging across adults aged 30–79 years. While traditional technologies were broadly preferred, digital and welfare technologies were less favored by the oldest cohort, and reasons for use varied by generation. Many attitudes, however, were shared, and most respondents reported keeping pace with technological developments. These insights should inform user-centered development and implementation of technologies that support active and healthy aging across the life course. Future research should examine longitudinal cohort and period effects, expand measures of actual usage and prior experience, and investigate cross-national differences and post-pandemic shifts in digital practices.

Key limitations include: low response rates, especially in younger cohorts, which may limit generalizability; potential self-selection bias favoring individuals more interested in technology given that 95% responded online; lack of detailed measures on actual technology use, prior experience, and specific knowledge of smart home technologies; the Swedish context with high Internet access among older adults may limit international generalizability; data collected before the COVID-19 pandemic may not reflect subsequent changes in digital shopping and service use; and challenges in predicting future preferences of younger generations over long horizons. Although the sample resembles the Swedish population on several characteristics, these limitations and potential biases should be considered when interpreting results.