Innovations in everyday technological solutions, such as wireless “tap and go” payments, artificial intelligence-based smart home devices, and biometric security measures, have substantially improved productivity and quality of life in modern Western societies (e.g. Alhassan and Adam, Reference Alhassan and Adam2021). More recently, there has been increasing interest in the utility of technological solutions to address the needs of a rapidly aging population, such as the use of sensors for falls identification and prevention, digital tests to rapidly screen for dementia risk, and video-based computer and mobile applications to help maintain social connections and engagement in leisure and health activities. These technological solutions aim to enhance the quality of life and enable older adults to continue living independently in their homes and communities for as long as possible, with effective self-management of health and medical issues (Baraković etal., Reference Baraković2020). This has potential to reduce the burden on formal and informal healthcare resources, prolong employment opportunities and productivity, and delay transitions to aged care facilities. However, while such technological advancements hold much potential, there are also non-trivial barriers that can limit their use. For example, lower familiarity with technology in older adults has been associated with computer-related anxiety, fear of age-related stereotypes, perceived inaccessibility and lack of usefulness of technology, perceived unavailability of informal technical support, and a lack of confidence in using devices (Mariano etal., Reference Mariano2021).
For people living with dementia, a range of assistive technological solutions has been developed to improve care and quality of life, such as medication management devices, voice-assisted software to set reminders, and robots for sensory stimulation (for an overview, see De Sousa, Reference De Sousa and Shankardass2021). While assistive technologies for people with dementia are increasing in popularity, and despite evidence that there is interest and a willingness to learn new technologies (e.g. tablets and their applications) for creative and cognitive stimulation, and relaxation activities (Lim etal., Reference Lim, Wallace, Luszcz and Reynolds2013), several important barriers to technology utilization need to be considered. The most commonly identified barriers for people with dementia, and for individuals with mild cognitive impairment (MCI), relate to declining cognitive function (particularly in memory, executive function, and language), neuropsychiatric symptoms, and ability to function independently in activities of daily living (Jekel etal., Reference Jekel2015; Monaci and Morris, Reference Monaci and Morris2012). Given the insidious nature of dementia, particularly Alzheimer’s disease (AD) dementia, these barriers may result in a gradual disengagement with assistive technology as the disease progresses, counter-productive to clinical goals of supporting independent living, meaningful engagement in activities, and self-management. It is therefore critical that not only are people with dementia provided adequate and appropriate support to integrate relevant technology into their daily lives but also that any assistive technology designed for people with dementia also considers the changing nature of the disease. For example, more detailed and complex instructions may be acceptable for individuals at the MCI stage, but may not be appropriate for individuals with moderate AD dementia. Similarly, sensory processing ability deteriorates in dementia (Dawes etal., Reference Dawes, Wolski, Himmelsbach, Regan and Leroi2019), meaning visual or audio stimuli used in assistive technology may need to be calibrated accordingly. The use of assistive technology for dementia has been accelerated by the COVID-19 pandemic, with an increased use of telehealth services to continue supporting people with dementia, and their families and carers. However, dementia-related deficits in memory and new learning pose additional challenges for attempts to train individuals in the use of technologies. Emerging studies are now investigating the efficacy of such training interventions and the extent to which learned skills transfer to individuals’ daily routines.
Kerkhof etal. (Reference Kerkhof, Bergsma, Mangiaracina, Planting, Graff and Dröes2021, this volume) reviewed 16 studies evaluating the efficacy of interventions aiming to train people with mild dementia to learn (or re-learn) how to use a range of technologies. The types of technologies across studies included information and communication technology (ICT) devices (e.g. smartphone and tablet applications, telephone voicemail, email, and internet searching), music and television devices (e.g. radio, CD/DVD players, remote controls), and activities involving household appliances (e.g. making tea/coffee, using a conventional or microwave oven, setting an alarm clock). The authors examined learning effects across different technology categories, methods of training used in the interventions, intensity and setting of the training interventions, study designs (e.g. randomized controlled trials versus group and single-case experimental or quasi-experimental studies), and type and severity of dementia syndromes. Participants included in the studies had confirmed or probable diagnoses of AD dementia, semantic dementia, or mixed dementia types, in the mild to moderately-severe stages. Due to significant heterogeneity in study design such as the training procedures and outcome measures, and the small sample sizes of the included studies (n = 14–161 across the group studies, n = 1–2 for case studies), effects of the interventions on performance with technology tasks were summarized in terms of percentage of correct responses or task steps completed without assistance.
While the concept of training people with MCI or dementia to learn new information or skills has been previously examined (albeit with mixed results; e.g. see Bahar-Fuchs, etal., Reference Bahar-Fuchs, Clare and Woods2013), Kerkhof and colleagues present the first synthesis of findings that specifically relate to learning to use technology. Overall, all 16 studies demonstrated positive effects of training interventions on task performance with the various technologies. For example, Kerkhof and colleagues reported an increase in average performance from 46% at baseline to 60% post-intervention across five group studies targeting learning of technologies to assist with independent activities of daily living (IADLs) such as using the stove, an alarm clock, or television and music devices, preparing tea, making a telephone call, or writing a shopping list. Interventions to teach other types of technology across a series of case studies appeared as or more effective, with performance more than doubling following the training in some cases.
Kerkhof and colleagues considered two main mechanisms by which people with dementia may learn new technologies or information – errorless learning (implicit) and errorful learning (explicit). The errorless or implicit memory approach may be most appropriate for training individuals with dementia to use technology or (re)learn activities of daily living, which often involve motor skill and pattern learning, and habit formation. This is typically achieved via repeated exposure to tasks, consistent practice, and reduced reliance on external feedback supported by explicit memory systems (van Halteren-van Tilborg, etal., Reference van Halteren-van Tilborg, Scherder and Hulstijn2007). Errorless learning methods include reducing participant guessing and immediately correcting errors, dividing tasks into smaller steps, physical demonstration of how task steps are to be completed, providing verbal or visual instructions, vanishing cues (gradually withheld with successful performance), and spaced retrieval (increased recall intervals with successful performance) (De Werd, etal., Reference De Werd, Boelen, Rikkert and Kessels2015). Successful implicit learning in dementia has been demonstrated with spatial and motor-based maze tasks, rotor-pursuit tasks, mirror-tracing tasks, and serial reaction time tasks (van Halteren-van Tilborg etal., Reference van Halteren-van Tilborg, Scherder and Hulstijn2007). It has also been used in cognitive rehabilitation programs and, more recently, been exploited to examine cognitive dysfunction in the very earliest stages of AD (Lim etal., Reference Lim, Baker, Bruns, Mills, Fowler, Fripp and Maruff2020).
In contrast, the errorful or explicit memory approach relies on the provision of feedback regarding errors made and allows for response guessing in order to learn from mistakes (i.e. trial-and-error; Śmigórska, etal., Reference Śmigórska, Śmigórski and Rymaszewska2019). In patients with cognitive impairments, learning via mistakes through errorful learning is proposed to increase susceptibility to conflating the synaptic activation patterns corresponding to correct and incorrect responses, rendering it a suboptimal method of learning (Śmigórska etal., Reference Śmigórska, Śmigórski and Rymaszewska2019).
No systematic advantage of errorless learning was observed over errorful learning in the reviewed studies. However, only 3 of the 16 studies incorporated both approaches, and the remaining studies only utilized errorless learning. Given the limited research examining specific aspects of externally provided (explicit) feedback on motor skill learning in dementia, van Halteren-van Tilborg etal. (Reference van Halteren-van Tilborg, Scherder and Hulstijn2007) concluded that people with AD dementia likely benefit from receiving constant visual feedback. In a study of errorless learning for everyday memory functions in six patients with AD dementia, Clare & Jones (Reference Clare and Jones2008) noted mixed attitudes towards the interventions among patients and their carers. As tailored interventions were supported by carers at home in between sessions, Clare and colleagues emphasized the need to consider individual factors such as patient motivation and the nature and quality of the patient-carer relationship, as potential moderators of intervention success. Errorless learning techniques have also been judged by some healthcare professionals to be time-consuming and staffing resource-intensive, and it is likely that the appropriateness and effectiveness of errorless learning methods may differ according to severity of disease (de Werd etal., Reference De Werd, Boelen, Rikkert and Kessels2015). Furthermore, errorless or implicit learning approaches are not ideal for forming new associations or engaging in conscious retrieval from long-term memory (Śmigórska etal., Reference Śmigórska, Śmigórski and Rymaszewska2019). Mixed learning models (e.g. combinations of implicit and explicit methods) may therefore be appropriate for certain patients. In order to properly quantify the benefits of implicit learning over and above explicit memory training paradigms, direct comparison of these approaches would be important for future technology training interventions in people with dementia. In particular, understanding differences in the longer-term maintenance of learned skills and how they are applied in individuals’ daily routines will be critical for evaluating the impact of these interventions.
Among the studies reviewed by Kerkhof and colleagues, maintenance of gains in performance was reported across technology tasks at follow-up intervals that ranged from 1.5 to 15 weeks in group studies of (re)learning technologies to assist with IADLs, 4–52 weeks in case studies of (re)learning to use handheld touchscreen devices, and 5–26 weeks in case studies that focused on (re)learning a variety of different technology tasks. Four case studies provided qualitative evidence that participants continued to use the learned technologies between 6 and 14 months after the end of the intervention. However, the settings in which training interventions took place (e.g. home or specialist centers) were not specified in half of the included studies. This is an important consideration as the long-term maintenance of technology use and generalizability from the training context to the home living environment is crucial for the sustained use of assistive technology to improve care and quality of life in people with dementia (van Halteren-van Tilborg etal., Reference van Halteren-van Tilborg, Scherder and Hulstijn2007). For example, does learning at home better facilitate the use of the technology at home because of the consistency of contextual cues? Similarly, do tests and interventions that are ecologically valid foster maximal translation into everyday life? This is particularly salient for lifestyle modification trials to prevent dementia, where participants often complete an intensive behavioral intervention that targets a range of modifiable risk factors for dementia online or in-clinic (e.g. FINGER trial, Ngandu etal., Reference Ngandu2015; Maintain Your Brain [MYB] trial, Heffernan etal., Reference Heffernan2019). While some cognitive benefit has been reported from these trials, adherence to such interventions is typically low (e.g. less than 20% of participants completing all intervention sessions; Turunen etal., Reference Turunen2019), and particularly as trials increase in complexity and intensity of the intervention components (Coley etal., Reference Coley2019). The extent to which behavior change is maintained in the longer term also remains unclear. This has given rise to the development of more ecologically valid and personalized interventions, such as RESPOND (Barker etal., Reference Barker2019) and the BetterBrains trial (Lim etal., Reference Lim2021), that seek to tailor interventions to participants’ specific risk factors and link them to community-based resources, with the aspiration of cultivating long-term behavioral change.
The need for greater person-centered interventions was also highlighted in Kerkhof and colleagues’ review. Well-designed and well-controlled trials are a critical ongoing step in optimizing how individuals can be supported to efficiently and effectively learn to integrate existing and emerging technologies into their daily lives. This support will likely need to be personalized depending on the severity of disease or cognitive impairment, the nature of the to-be-learned material, and the availability of family members or carers to assist. Exposing older adults to new technologies as they emerge will be important to establish familiarity early. Optimizing the parameters for learning to use technology in older adults with MCI and dementia will likely benefit future remotely administered cognitive rehabilitation and other health interventions. Reducing variability in patients’ competence in using technological devices may increase confidence that positive performance differences between intervention groups are directly related to the training programs themselves.
Digital technologies have significant potential to expand the reach of clinical services and research studies (especially in remote or rurally located individuals), improve time and cost effectiveness of assessments and therapeutic interventions for dementia, support and promote physical and mental wellbeing, provide information and support to informal carers, monitor a person’s needs in real-time and ensure their safety, and allow people with dementia to live more independently and autonomously in their homes. The positive findings reported by Kerkhof and colleagues provide reassurance that people with dementia can be effectively taught to use a range of technologies with great existing and emerging potential for enhancing functional ability and quality of life in this patient population. The important design features discussed here will need to be considered in future trials for the successful implementation of such technologies.
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Innovations in everyday technological solutions, such as wireless “tap and go” payments, artificial intelligence-based smart home devices, and biometric security measures, have substantially improved productivity and quality of life in modern Western societies (e.g. Alhassan and Adam, Reference Alhassan and Adam2021). More recently, there has been increasing interest in the utility of technological solutions to address the needs of a rapidly aging population, such as the use of sensors for falls identification and prevention, digital tests to rapidly screen for dementia risk, and video-based computer and mobile applications to help maintain social connections and engagement in leisure and health activities. These technological solutions aim to enhance the quality of life and enable older adults to continue living independently in their homes and communities for as long as possible, with effective self-management of health and medical issues (Baraković etal., Reference Baraković2020). This has potential to reduce the burden on formal and informal healthcare resources, prolong employment opportunities and productivity, and delay transitions to aged care facilities. However, while such technological advancements hold much potential, there are also non-trivial barriers that can limit their use. For example, lower familiarity with technology in older adults has been associated with computer-related anxiety, fear of age-related stereotypes, perceived inaccessibility and lack of usefulness of technology, perceived unavailability of informal technical support, and a lack of confidence in using devices (Mariano etal., Reference Mariano2021).
For people living with dementia, a range of assistive technological solutions has been developed to improve care and quality of life, such as medication management devices, voice-assisted software to set reminders, and robots for sensory stimulation (for an overview, see De Sousa, Reference De Sousa and Shankardass2021). While assistive technologies for people with dementia are increasing in popularity, and despite evidence that there is interest and a willingness to learn new technologies (e.g. tablets and their applications) for creative and cognitive stimulation, and relaxation activities (Lim etal., Reference Lim, Wallace, Luszcz and Reynolds2013), several important barriers to technology utilization need to be considered. The most commonly identified barriers for people with dementia, and for individuals with mild cognitive impairment (MCI), relate to declining cognitive function (particularly in memory, executive function, and language), neuropsychiatric symptoms, and ability to function independently in activities of daily living (Jekel etal., Reference Jekel2015; Monaci and Morris, Reference Monaci and Morris2012). Given the insidious nature of dementia, particularly Alzheimer’s disease (AD) dementia, these barriers may result in a gradual disengagement with assistive technology as the disease progresses, counter-productive to clinical goals of supporting independent living, meaningful engagement in activities, and self-management. It is therefore critical that not only are people with dementia provided adequate and appropriate support to integrate relevant technology into their daily lives but also that any assistive technology designed for people with dementia also considers the changing nature of the disease. For example, more detailed and complex instructions may be acceptable for individuals at the MCI stage, but may not be appropriate for individuals with moderate AD dementia. Similarly, sensory processing ability deteriorates in dementia (Dawes etal., Reference Dawes, Wolski, Himmelsbach, Regan and Leroi2019), meaning visual or audio stimuli used in assistive technology may need to be calibrated accordingly. The use of assistive technology for dementia has been accelerated by the COVID-19 pandemic, with an increased use of telehealth services to continue supporting people with dementia, and their families and carers. However, dementia-related deficits in memory and new learning pose additional challenges for attempts to train individuals in the use of technologies. Emerging studies are now investigating the efficacy of such training interventions and the extent to which learned skills transfer to individuals’ daily routines.
Kerkhof etal. (Reference Kerkhof, Bergsma, Mangiaracina, Planting, Graff and Dröes2021, this volume) reviewed 16 studies evaluating the efficacy of interventions aiming to train people with mild dementia to learn (or re-learn) how to use a range of technologies. The types of technologies across studies included information and communication technology (ICT) devices (e.g. smartphone and tablet applications, telephone voicemail, email, and internet searching), music and television devices (e.g. radio, CD/DVD players, remote controls), and activities involving household appliances (e.g. making tea/coffee, using a conventional or microwave oven, setting an alarm clock). The authors examined learning effects across different technology categories, methods of training used in the interventions, intensity and setting of the training interventions, study designs (e.g. randomized controlled trials versus group and single-case experimental or quasi-experimental studies), and type and severity of dementia syndromes. Participants included in the studies had confirmed or probable diagnoses of AD dementia, semantic dementia, or mixed dementia types, in the mild to moderately-severe stages. Due to significant heterogeneity in study design such as the training procedures and outcome measures, and the small sample sizes of the included studies (n = 14–161 across the group studies, n = 1–2 for case studies), effects of the interventions on performance with technology tasks were summarized in terms of percentage of correct responses or task steps completed without assistance.
While the concept of training people with MCI or dementia to learn new information or skills has been previously examined (albeit with mixed results; e.g. see Bahar-Fuchs, etal., Reference Bahar-Fuchs, Clare and Woods2013), Kerkhof and colleagues present the first synthesis of findings that specifically relate to learning to use technology. Overall, all 16 studies demonstrated positive effects of training interventions on task performance with the various technologies. For example, Kerkhof and colleagues reported an increase in average performance from 46% at baseline to 60% post-intervention across five group studies targeting learning of technologies to assist with independent activities of daily living (IADLs) such as using the stove, an alarm clock, or television and music devices, preparing tea, making a telephone call, or writing a shopping list. Interventions to teach other types of technology across a series of case studies appeared as or more effective, with performance more than doubling following the training in some cases.
Kerkhof and colleagues considered two main mechanisms by which people with dementia may learn new technologies or information – errorless learning (implicit) and errorful learning (explicit). The errorless or implicit memory approach may be most appropriate for training individuals with dementia to use technology or (re)learn activities of daily living, which often involve motor skill and pattern learning, and habit formation. This is typically achieved via repeated exposure to tasks, consistent practice, and reduced reliance on external feedback supported by explicit memory systems (van Halteren-van Tilborg, etal., Reference van Halteren-van Tilborg, Scherder and Hulstijn2007). Errorless learning methods include reducing participant guessing and immediately correcting errors, dividing tasks into smaller steps, physical demonstration of how task steps are to be completed, providing verbal or visual instructions, vanishing cues (gradually withheld with successful performance), and spaced retrieval (increased recall intervals with successful performance) (De Werd, etal., Reference De Werd, Boelen, Rikkert and Kessels2015). Successful implicit learning in dementia has been demonstrated with spatial and motor-based maze tasks, rotor-pursuit tasks, mirror-tracing tasks, and serial reaction time tasks (van Halteren-van Tilborg etal., Reference van Halteren-van Tilborg, Scherder and Hulstijn2007). It has also been used in cognitive rehabilitation programs and, more recently, been exploited to examine cognitive dysfunction in the very earliest stages of AD (Lim etal., Reference Lim, Baker, Bruns, Mills, Fowler, Fripp and Maruff2020).
In contrast, the errorful or explicit memory approach relies on the provision of feedback regarding errors made and allows for response guessing in order to learn from mistakes (i.e. trial-and-error; Śmigórska, etal., Reference Śmigórska, Śmigórski and Rymaszewska2019). In patients with cognitive impairments, learning via mistakes through errorful learning is proposed to increase susceptibility to conflating the synaptic activation patterns corresponding to correct and incorrect responses, rendering it a suboptimal method of learning (Śmigórska etal., Reference Śmigórska, Śmigórski and Rymaszewska2019).
No systematic advantage of errorless learning was observed over errorful learning in the reviewed studies. However, only 3 of the 16 studies incorporated both approaches, and the remaining studies only utilized errorless learning. Given the limited research examining specific aspects of externally provided (explicit) feedback on motor skill learning in dementia, van Halteren-van Tilborg etal. (Reference van Halteren-van Tilborg, Scherder and Hulstijn2007) concluded that people with AD dementia likely benefit from receiving constant visual feedback. In a study of errorless learning for everyday memory functions in six patients with AD dementia, Clare & Jones (Reference Clare and Jones2008) noted mixed attitudes towards the interventions among patients and their carers. As tailored interventions were supported by carers at home in between sessions, Clare and colleagues emphasized the need to consider individual factors such as patient motivation and the nature and quality of the patient-carer relationship, as potential moderators of intervention success. Errorless learning techniques have also been judged by some healthcare professionals to be time-consuming and staffing resource-intensive, and it is likely that the appropriateness and effectiveness of errorless learning methods may differ according to severity of disease (de Werd etal., Reference De Werd, Boelen, Rikkert and Kessels2015). Furthermore, errorless or implicit learning approaches are not ideal for forming new associations or engaging in conscious retrieval from long-term memory (Śmigórska etal., Reference Śmigórska, Śmigórski and Rymaszewska2019). Mixed learning models (e.g. combinations of implicit and explicit methods) may therefore be appropriate for certain patients. In order to properly quantify the benefits of implicit learning over and above explicit memory training paradigms, direct comparison of these approaches would be important for future technology training interventions in people with dementia. In particular, understanding differences in the longer-term maintenance of learned skills and how they are applied in individuals’ daily routines will be critical for evaluating the impact of these interventions.
Among the studies reviewed by Kerkhof and colleagues, maintenance of gains in performance was reported across technology tasks at follow-up intervals that ranged from 1.5 to 15 weeks in group studies of (re)learning technologies to assist with IADLs, 4–52 weeks in case studies of (re)learning to use handheld touchscreen devices, and 5–26 weeks in case studies that focused on (re)learning a variety of different technology tasks. Four case studies provided qualitative evidence that participants continued to use the learned technologies between 6 and 14 months after the end of the intervention. However, the settings in which training interventions took place (e.g. home or specialist centers) were not specified in half of the included studies. This is an important consideration as the long-term maintenance of technology use and generalizability from the training context to the home living environment is crucial for the sustained use of assistive technology to improve care and quality of life in people with dementia (van Halteren-van Tilborg etal., Reference van Halteren-van Tilborg, Scherder and Hulstijn2007). For example, does learning at home better facilitate the use of the technology at home because of the consistency of contextual cues? Similarly, do tests and interventions that are ecologically valid foster maximal translation into everyday life? This is particularly salient for lifestyle modification trials to prevent dementia, where participants often complete an intensive behavioral intervention that targets a range of modifiable risk factors for dementia online or in-clinic (e.g. FINGER trial, Ngandu etal., Reference Ngandu2015; Maintain Your Brain [MYB] trial, Heffernan etal., Reference Heffernan2019). While some cognitive benefit has been reported from these trials, adherence to such interventions is typically low (e.g. less than 20% of participants completing all intervention sessions; Turunen etal., Reference Turunen2019), and particularly as trials increase in complexity and intensity of the intervention components (Coley etal., Reference Coley2019). The extent to which behavior change is maintained in the longer term also remains unclear. This has given rise to the development of more ecologically valid and personalized interventions, such as RESPOND (Barker etal., Reference Barker2019) and the BetterBrains trial (Lim etal., Reference Lim2021), that seek to tailor interventions to participants’ specific risk factors and link them to community-based resources, with the aspiration of cultivating long-term behavioral change.
The need for greater person-centered interventions was also highlighted in Kerkhof and colleagues’ review. Well-designed and well-controlled trials are a critical ongoing step in optimizing how individuals can be supported to efficiently and effectively learn to integrate existing and emerging technologies into their daily lives. This support will likely need to be personalized depending on the severity of disease or cognitive impairment, the nature of the to-be-learned material, and the availability of family members or carers to assist. Exposing older adults to new technologies as they emerge will be important to establish familiarity early. Optimizing the parameters for learning to use technology in older adults with MCI and dementia will likely benefit future remotely administered cognitive rehabilitation and other health interventions. Reducing variability in patients’ competence in using technological devices may increase confidence that positive performance differences between intervention groups are directly related to the training programs themselves.
Digital technologies have significant potential to expand the reach of clinical services and research studies (especially in remote or rurally located individuals), improve time and cost effectiveness of assessments and therapeutic interventions for dementia, support and promote physical and mental wellbeing, provide information and support to informal carers, monitor a person’s needs in real-time and ensure their safety, and allow people with dementia to live more independently and autonomously in their homes. The positive findings reported by Kerkhof and colleagues provide reassurance that people with dementia can be effectively taught to use a range of technologies with great existing and emerging potential for enhancing functional ability and quality of life in this patient population. The important design features discussed here will need to be considered in future trials for the successful implementation of such technologies.