In daily life and society, Information Communication Technologies like the internet, smartphones, tablets, and applications (apps) have become ubiquitous. Proficiency in these technologies and broad digital competencies are important assets for participation in the working world (Oberländer et al., 2020). The concept of “digital competence” was recognized as one of the eight core competencies for lifelong learning by the European Parliament and Council as early as 2006 (European Parliament, 2006). Its significance extends beyond the professional world since the activities of daily living are increasingly shaped by digitalization. In numerous aspects of our lives, digital technologies have emerged as the most convenient means of access. For example, in the realm of transportation and travel, we simply call an Uber via an app or find the nearest subway station with the “maps” application on our smartphones. These digital approaches offer advantages, including flexibility and mobility (Quamar et al., 2020). Arguably, one of the most pivotal roles played by modern digital technologies is in the domain of communication. Instant messaging (e.g., WhatsApp, Messenger), email services, social networking platforms (e.g., Instagram, Facebook) and video conferencing (e.g., Skype, Zoom) nowadays are common (Quamar et al., 2020). Typically, these communication tools are accessed through the use of smartphones.

The role of participation in digital opportunities is particularly evident across different demographic groups. Among the younger population, aged between 20 and 25, digital tools have emerged as the primary medium for communication. In fact, owning a smartphone is considered by this age group as an almost indispensable component of social interaction, and those without such a device are perceived to be partially excluded from these interactions (Möller, 2016). A longitudinal study in a Finnish sample showed that also in middle-aged and older persons, the perceived necessity to own and use information and communication technology (such as smartphones and tablets) was growing (Wilska and Kuoppamäki, 2017). In the case of older adults and individuals with medical conditions, especially in the context of eHealth and mHealth (i.e., the provision of healthcare services through Information Communication Technologies, particularly smart mobile devices), the significance of these technologies has been steadily growing. As preventive measures or complements to traditional medical care, mobile health apps are becoming increasingly accessible via smartphones or tablets. Unfortunately, the adoption of smart mobile devices is still less prevalent in older age groups, even though older adults may benefit the most from telemedical apps and mHealth communication (Chiarini et al., 2013; Li et al., 2014; Changizi and Kaveh, 2017). To close these gaps there is a need to analyze potential factors contributing to non-use.

One important factor for non-use or inappropriate use presents an inadequate understanding of how to properly operate these devices. There are several potential challenges older adults might face when attempting to navigate digital devices. First, there appear good news when looking at overall usability of smartphones and tablets. Kortum and Sorber (2015) who investigated usability ratings of the most popular applications on iOS and Android OS among more than 3,000 participants reported high usability ratings. Also in the older population, there is a positive reception of smart mobile technologies: in a recent study, Brunzini et al. (2023) found that older Italian citizens regarded digital devices, including smartphones and tablets, as quite useable and learnable. Moreover, their small pilot sample demonstrated only few errors when operating these devices for social support, and entertainment purposes. However, usability and performance measures frequently seem to dissociate in older adults. For example, in a study comparing touchscreen versus keyboard use in two tasks, Sonderegger et al. (2016) found that while older adults were equally effective at solving text input- and menu selection-tasks as their younger counterparts, they performed less efficient. At the same time the perceived usability of smartphones was rather positive in older adults. Multiple obstacles faced by senior citizens were identified by McGaughey et al. (2013) or Gomez-Hernandez et al. (2023) in their reviews: Some difficulties can be attributed to the device itself, such as the small size of the gadget, others depend on characteristics of the user, such as physical and cognitive limitations or a lack of confidence and training. Furthermore, studies suggest that age, together with educational background, may have an influence on the ability to solve technology-associated problems (Ertl et al., 2020).

However, non-use due to reduced competencies does not merely pertain to healthy older adults, but also to persons with cognitive disabilities, for example after stroke. We propose that digital tool competencies is also a highly relevant topic in the context of neurorehabilitation. Strikingly, limb apraxia, known as a disorder of (traditional) tool use (Goldenberg, 2013; Randerath, 2023), is a frequent consequence of brain damage such as stroke with a prevalence of 28–37% among stroke survivors (Donkervoort et al., 2000). The term “limb apraxia” refers to disorders of learned and purposeful movements (Liepmann, 1900; Heilman and Rothi, 1993). When applying the traditional tool use assessments using the DILA-S in stroke patients (Buchmann and Randerath, 2017; Buchmann et al., 2020a,b), our patients’ left us with the impression that next to their common tool competencies (i.e., how to use a fork or a toothbrush), their digital tool competencies (i.e., send a note or picture to their relatives using a messenger-application) are just as important to them for their ADLs (activities of daily living) and participation. From our observations, there are valid concerns surrounding the capacity of stroke patients to navigate digital devices. Lastly, apraxia is only one of a vast variety of potential syndromes and disorders after stroke that may affect digital tool use. Other stroke-associated symptoms affecting motor, perceptual, communicative, or cognitive abilities such as hemiplegia, hemineglect, aphasia, and deficits concerning concentration and memory are potential influencing factors that also may detrimentally impact digital competencies. Another concern regarding the capacity of stroke patients to operate digital devices relates to the advanced age of many individuals in this patient group (Busch and Kuhnert, 2017). Therefore, it is important to first investigate the digital tool use competencies in healthy older adults.

Considering the profound impact of Information Communication Technologies on ADLs, in their review Quamar et al. (2020) conclude that it “marks a paradigm shift in the way we assess and measure everyday functioning”. The digitalization drives the need for standardized tests of basic digital skills to be considered for ADL assessments, contributing to the “paradigm shift”.

The assessment of individual difficulties in common digital tool competencies seems an important step towards characterizing an individual’s problem also before offering a tailored training intervention. There are strong efforts to enhance digital accessibility for the older population, e.g., by designing special user interfaces for older adults (Arab et al., 2013; Sakdulyatham et al., 2017) or providing smartphone training classes (Zhao et al., 2020). A standardized assessment could be useful to evaluate the success of such an intervention. Despite the decent amount of tests for general or specific technological knowledge and skills among high school and college students (for an overview see Covello and Lei, 2010), standardized instruments for the assessment of digital competencies in the general population are scarce. Existing assessments rely on self-report questionnaires rather than practical tasks (Ferrari, 2013; Lu et al., 2017; Karnoe et al., 2018; Zhao et al., 2020) or they focus on device usability (Sonderegger et al., 2016; Brunzini et al., 2023).

Inspired by our clinical work, we developed a novel pragmatic assessment for digital tool competencies. The major goal of this manuscript is to introduce the so-called DIGI (DIGItal tools test). This instrument aims to assess fundamental digital tool competencies focusing on elementary tasks associated with the utilization of smart mobile devices, namely smartphones and tablets. It evaluates participants’ performance regarding their ability to select an adequate application (selection), successful navigation inside the application (production) and minimize motor-related errors (motor error). The DIGI has been developed especially for use in older adults and neurologic patients. In the present pilot study, we sought to test the feasibility and acceptance of the DIGI in a sample of healthy young and older adults participating in the assessment. The study further involved a comparative analysis between the two cohorts and included a correlational analysis with performance in the traditional novel and familiar tool use tests of the Diagnostic Instrument for Limb Apraxia (DILA-S).

The study was approved by the ethics committee of the University of Konstanz (#15/2020) and conducted in accordance with the declaration of Helsinki. All participants gave informed consent before taking part in the study. Post hoc power-analyses can be found in the Supplementary material, Table 1.

In the present work, we introduced the DIGI, an assessment tool for evaluating common competencies in handling smartphones and tablets. Through a pilot test involving a small sample of young and older adults, we demonstrated good interrater reliability, feasibility and acceptability of the DIGI assessment. We further showed its potential to detect performance differences in digital tool competencies between younger and older adults.

Our finding suggests that older adults might understand as proficiently as younger adults which application suits best for the assigned task. The older group was able to find and tap the appropriate app-icon on the mobile device and there were no differences between age groups in terms of selection scores.

However, consistent with our hypothesis, the older adults exhibited significantly more problems in producing the correct steps while navigating within the apps. This became evident in group differences for the DIGI production- and movement-related error scores for both smartphone and tablet. Common movement-related errors included, for example, imprecise typing whenever entering text and misperceptions about the meaning of a digital gesture, such as confusing typing and swiping when answering a phone call or confusing the zoom-in and the zoom-out gesture. It seems unlikely that this deficit could be explained by a decreased hand sensibility in the older age group since we observed that hand sensibility did not differ between groups. The literature, however, demonstrates that older adults show indeed a variety of motor deficits in comparison to younger adults, such as difficulties in coordination, increased variability of movements, slowing of movements, and difficulties with balance and gait, which are attributable to age-related changes in the central nervous system (for an overview see Seidler et al., 2010). These age-related changes in the central nervous system might have contributed to the increase in motor-related errors in the older age group. Early technology-related findings by Smith et al. (1999) support this hypothesis. The authors showed that cursor control tasks with a computer mouse were significantly more difficult for older than younger adults. In their study, this difficulty was associated with age-related declines in motor control, specifically in motor coordination. Comparable mechanisms might have led to the observation of more movement-related errors in the older group of the current study.

Furthermore, in the older age group, the ability to use digital tools correlated with the ability to use traditional (mechanical) but novel tools. Specifically, individuals with lower skills in navigating digital tools tend to display lower skills in applying novel tools to their recipient objects. This could point towards three different interpretations. One hypothesis posits that lower digital tool competencies are indicative of cognitive decline due to healthy aging. Substantiating this hypothesis, the existing literature demonstrates that digital app usage including such characteristics as number of apps used, usage by hour of day, swipes, and keystroke events predicts cognitive ability in older adults as measured with neuropsychological assessments (Gordon et al., 2019). The second hypothesis could point towards healthy older people having overall difficulties in novel hand-tool interactions in the sense of mechanical reasoning and thereby showing lower practical digital tool competencies. While previous results (Randerath et al., 2017) suggest that healthy older versus young subjects do not differ on a group level in performing novel tool use, the current study demonstrates a correlation between novel tool use and digital tool use skills. The third hypothesis directs towards an effect of the cohort with reduced familiarity with digital rules. Older people, who did not grow up surrounded by digital technologies, are sometimes labeled as “digital immigrants” (Prensky, 2001). They may need similar resources to handle digital tools as they need for using traditional (mechanical) novel tools. This may relate to general rule retrieval that is also discussed to be essential for novel tool utilization (Randerath, 2020; Stoll et al., 2022). The Broca area may be a relevant neural correlate that has been associated with different behavioral tasks based on rules, such as rule-guided actions (Bunge, 2004; Donohue et al., 2008), and grammatical rules in language syntax (Tettamanti et al., 2002). An overlap of lesion areas associated with impaired novel tool selection in Broca’s area have been discussed to be related to the retrieval and maintenance of object characteristics and physic rules (Stoll et al., 2022). The speculated potential overlap of digital tool competencies and behavioral and neural correlates of rule retrieval and novel tool use needs to be addressed in future studies. The argument that digital immigrants who encountered digital technology much later in life may approach these devices like novel tools is in line with the third hypothesis. Instead, younger people, commonly referred to as digital natives, may use different resources for digital competencies, relying more on common knowledge and overlearned procedures. In accordance with the hypothesis that the brains of digital natives might diverge from those of digital immigrants (Prensky, 2001), we speculate that the younger and the older age groups in our study might have recruited different areas of the brain to solve the DIGI. Participants in the older age group might have employed similar brain regions to solve the DIGI as they do to solve the DILA-S NTT. Our data implies that subsequent studies on the DIGI’s psychometric properties need to clarify its underlying constructs cohort-wise as age and the year born may both play a decisive role.

Furthermore, in our study, all participants utilized laboratory-owned smartphones and tablets rather than their personal devices. While this has several practical reasons (standardization, data protection, assessment procedure etc.) there are also some challenges going along with this. For example, a participant might have been familiar with the Android OS in general but running on a Huawei smartphone, and therefore, may not have been versed in its operation on a Samsung device, specifically on a Samsung Galaxy A7 used in the current setup. Dealing with unfamiliar devices can lead to user errors, given variations in the design and operation of different smartphones and tablets (Byrom and Row, 2017; Germine et al., 2019) and perhaps younger participants are more flexible in switching between brands.

It appears notable that the here-described difficulties in handling digital tools in the older sample may further extend to potential non-use of more specific health apps. The question arises of how to secure the inclusion and participation of those suffering from a loss of digital competencies. Digital tool use has gained growing importance not only for the area of health improvement but also in medical diagnostics. For example, current literature discusses approaches that target cognitive digital phenotyping by capturing everyday cognition in vivo via digital tool use (Hackett and Giovannetti, 2022). As some studies suggest that app use can predict cognitive performance decline (Gordon et al., 2019), the idea of cognitive digital phenotyping would be, for example, to contribute to early diagnosis of dementia by evaluating a person’s app using behavior (Hackett and Giovannetti, 2022). The inevitable growth in these approaches promises increasing gains and advantages but faces many challenges including participation of vulnerable groups.

There are certain methodological limitations and challenges when assessing digital tool competencies such as the handling of smartphones and tablets. It is important to keep the experimental devices in an up-to-date state to ensure their optimal functionality. However, this practice can introduce concerns regarding the comparability of early and later DIGI surveys, since the software, and the UI might change slightly with updates. Similarly, hardware, software, and the way we use it changes rapidly, which may pose a difficulty in the context of the thorough development of a neuropsychological diagnostic instrument (Schmand, 2019). Thus, it is questionable how long the specific tasks included in the DIGI will be relevant for our everyday living. Additionally, it is debatable for how long the specific smartphone−/tablet-brands we included in the DIGI will remain among the most frequently used ones. While we here provide a framework for presenting items and evaluating practical digital competencies of common tasks and features of smartphones and tablets, for future developments, we expect that regular reevaluations and adjustments of the items and devices present necessary steps.

Specific limitations of the current study are the small sample sizes and ceiling effects in certain DIGI and DILA-S variables especially in the young adults group. A major objective for future research is to enlarge our samples for all age groups and incorporate conditions with constraint hand use to obtain control samples for neurologic patients who oftentimes suffer from motor unimanual impairments such as hemiparesis. For our neurologic sample, it will be important to broaden the sample and include more severely impaired patients. The next steps entail collecting psychometric data and evaluating behavioral and neural correlates of diminished digital competencies.

In light of the growing importance of digital devices, we tried to provide one important step towards diagnosing common digital abilities. In the present paper, we introduced an assessment instrument for basic competencies in smartphone and tablet use, the DIGI. We demonstrated its feasibility and acceptability in healthy samples of different ages. Differences between older and younger adults were found particularly for navigation within apps and for producing motor-related errors. Only in older adults worse performance in handling traditional novel tools in the DILA-S went along with reduced digital tool competencies in the DIGI. We speculated that the overlap of digital tool competencies and novel tool use is due to shared correlates of potential rule retrieval.

Follow-up studies should evaluate the DIGI’s psychometric properties in larger groups including samples of healthy older participants as well as participants with cognitive impairments such as after suffering from a stroke. To further elucidate the underlying mechanisms of digital tool competencies, future studies should combine behavioral and neuroimaging techniques. When investigating digital tool competencies it appears particularly important to consider age and year of birth.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by University of Konstanz ethics committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

SS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Visualization, Writing – original draft. IB: Data curation, Methodology, Project administration, Writing – review & editing. KH: Data curation, Formal analysis, Visualization, Writing – original draft. JL: Data curation, Methodology, Writing – review & editing. VL: Writing – review & editing, Data curation. FG: Data curation, Writing – review & editing. CH: Data curation, Writing – review & editing, Conceptualization, Methodology. GK: Writing – review & editing. SK: Writing – review & editing. JR: Writing – review & editing, Funding acquisition, Methodology, Resources, Supervision.