Correspondence to Dr Rasita Vinay; [email protected]
Strengths and limitations of this study
The study conducted an extensive search across five electronic databases spanning a decade to identify relevant literature on digital assistive technologies (DATs) and their impact on the quality of life (QOL) for people with dementia.
By excluding conference proceedings, book chapters, pilot and feasibility studies, the review might have missed ongoing or planned research that could offer insights into different DATs or QOL impacts.
While the scoping review approach allowed for a broad overview, it did not assess the quality of included studies or intervention effectiveness, potentially introducing bias and limiting in-depth analysis.
The study’s emphasis on patient-facing DATs could have introduced bias, highlighting digital therapeutics in the included literature while potentially overlooking other assistive technology categories.
Introduction
Background
In 2023, more than 55 million individuals worldwide are affected by dementia, with approximately 10 million new cases diagnosed yearly.1 Dementia encompasses various impairments regarding language, memory, cognition and the ability to perform daily activities.1 It progressively worsens over time and primarily affects individuals over 65, however can also affect those younger than 65, known as young onset dementia.1 Globally, dementia currently ranks as the seventh leading cause of death, significantly contributing to disability and dependency among the older population.1 The changing demographic landscape presents difficulties for caregivers and our healthcare system. As a result, there is a growing focus on using digital assistive technologies (DATs) to address these challenges and help sustain the independence of people with dementia (PWD).2
DAT is an umbrella term covering technologies used for education and rehabilitation, to overcome participation and activity restrictions, and to improve cognitive, sensory and motor abilities. It encompasses any technology that empowers individuals with functional constraints in everyday routines, educational pursuits, occupational endeavours or recreational engagements.3 DATs offer a valuable means for individuals and caregivers to manage various aspects of their daily routines effectively. They hold great promise for the care and support of PWD, and to alleviate the challenges associated with caregiving, such as maintaining autonomy and dignity.4 Here, autonomy can be understood as the principle for self-governance and decision making, where capacity is closely related to consent. However, as PWD progress along their dementia journey, their decision-making capacity may start to be impacted, and therefore maintaining dignity shifts as the greater value. Therefore, both these concepts require a commitment from caregivers in order to provide a quality of care, and hence also form important considerations from rising DATs.
Recently, technological advancements have paved the way for creating devices and applications that leverage sensory data tailored specifically for PWD. Notably, smartphones and wearables are now being employed to track physical activities, enabling in-home care assistance4 and serving as location trackers for monitoring wandering behaviour.5 Moreover, the emergence of artificial intelligence has led to the development of social assistive robots. These robots are designed to provide companionship and engage in therapeutic activities, such as the robotic seal Paro, which serves as an illustrative example.6 These technologies extend beyond basic assistance with daily tasks; they also contribute to the preservation of social interactions, memory support, participation in leisure activities, location tracking and health monitoring.2 7
Maintaining a good quality of life (QOL) is essential for PWD and must be considered when assessing the impact of DATs. QOL encompasses physical and mental well-being and extends to social and emotional dimensions (eg, emotional stability, social integration or self-esteem).8 Various tools such as questionnaires and self-assessment scales measure the overall perceived QOL; activity-based assessments or cognitive status evaluations serve as diverse means for quantifying QOL.9 Consequently, these measures and evaluation instruments should be regarded as guiding tools in determining the QOL experienced by PWD.
While the current literature focuses on specific classes of DATs, often highlighting particular outcomes, there is a lack of a comprehensive overview of the different DATs and their impact on the QOL of PWD. This scoping review aims to fill this gap.
Objectives
The primary aim of this scoping review is to provide an overview of DATs for PWD and the ways in which they influence an individual’s QOL. Therefore, the following research question was formulated: ‘What is the impact of DATs on the QOL for people with dementia?’
In this scoping review, the opportunities of DATs and their role in enhancing caregiving practices and the QOL of PWD were investigated. Through a compilation of prevailing literature, this review can enhance the decision-making process by facilitating stakeholders’ comprehension of the spectrum of accessible DATs and their efficacy in effectively elevating the QOL as a secondary objective.
Methods
Search strategy
The methodological framework proposed by Arksey and O’Malley10 and the recommendations from PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation11 were adopted to conduct this scoping review. A protocol for the scoping review was published on 13 December 202312 and the study was preregistered on the Open Science Framework Registry on 5 May 2023 (https://osf.io/zcnx8/).
Search terms were derived from a preliminary search and analysed by comparing the words found in titles, abstracts and keywords, following a ‘patient/population, intervention, outcome’ (PIO) concept. Additionally, to enhance the accuracy and comprehensiveness of the search results, all authors were involved in a consensus process, and an additional expert was consulted to validate the identified terms and suggest any additional relevant keywords. A comprehensive search was performed on 17 May 2023 across five electronic databases: Cochrane, Embase, PubMed, Scopus and Web of Science, to locate published literature surrounding the research question. To focus on recent technological advancements, only articles published between 1 January 2013 and 17 May 2023 were considered, allowing for a more up-to-date review. The search terms using the PIO table and full electronic search strategy can be seen in online supplemental file 1.
Inclusion and exclusion criteria
The articles were screened following specific inclusion and exclusion criteria, established by all authors and consolidated by an additional expert. Table 1 shows the eligibility criteria to ensure the relevance of the included studies to the research question.
Table 1Inclusion and exclusion criteria
| Inclusion criteria | Exclusion criteria |
|
|
DATs, digital assistive technologies; PWD, people with dementia.
Screening, data extraction and analysis
The search results were extracted and uploaded onto a literature review software, Rayyan (www.rayyan.ai) for screening. From May to June 2023, CS and RV screened all eligible articles’ titles and abstracts to determine their suitability for a full-text review according to the inclusion and exclusion criteria. Potential discrepancies were discussed between the authors and resolved through discussion and consensus. CS piloted the data extraction using five articles; checked and consulted by RV. An example of the data extraction form is described in online supplemental file 2. A full-text review of the final sample of included studies was conducted by CS and further consolidated by RV. A critical appraisal of individual sources of evidence was not done for this scoping review as it is beyond the scope of this article.
A narrative synthesis accompanied by frequency analysis was performed of the included literature to present findings on (1) author locations, (2) study approach, (3) type of article (4) study locations, (5) digital health technology (DHT) categories (see Coding strategy), (6) target population and (7) instruments measuring QOL. The QOL measures were initially evaluated according to the protocol.12 Based on a pilot of the extraction of the articles, it became obvious that there was rarely one distinct ‘QOL instrument’ used throughout all articles. However, these metrics were discovered to be excessively diverse and unsuitable to be reported consistently, leading to their omission. Instead, indirect outcomes, which also influence the QOL, were additionally recorded (eg, activity instruments, cognitive status, rating of the individual’s QOL, etc9). Furthermore, the protocol outlined the analysis of the type of DATs and sensory distribution channels.12 During the extraction of the articles, it became evident that the type of DATs and sensory distribution channels would not provide consistent reporting across the studies, and were instead replaced with DHT categories. Several studies discussed different DATs employing distinct sensory distribution channels, making it impossible to provide a uniform report, resulting in their exclusion.
Coding strategy
The codes for QOL measures/outcomes were generated by CS and RV based on thematic analysis.13 The Flanagan Quality of Life Scale served as the basis for the code, which forms the general ideas about concepts for evaluating the QOL.14 In addition, QOL scales such as QOL-AD and DQOL were explored to identify relevant concepts for PWD.15 Physical, social and environmental concepts were also included to have a broader impact on QOL than just health-related QOL.16 Ethical considerations were integrated into the coding strategy by drawing on principles of biomedical ethics (ie, dimensions such as autonomy and dignity).17 Furthermore, it should be noted that the priorities of these principles may shift as an individual’s dementia progresses. For instance, autonomy might be more important for individuals with mild dementia, while maintaining dignity becomes paramount for those with severe dementia. This consideration enabled providing a holistic overview of QOL dimensions across all stages of dementia, aligning with the evolving ethical dimensions of care. CS applied the codes to the articles using the ATLAS application (www.atlasti.com), and uncertainties were resolved by RV.
All the included studies were initially categorised using an inductively created classification scheme. However, it became apparent that this method was inadequate for representation purposes. Subsequently, the classification scheme was deductively realigned with the Digital Therapeutics (DTx) Alliance categories, and all the studies were reclassified accordingly.
The DTx Alliance introduced eight (industry-facing and admin-facing, healthcare provider and patient-facing) DHT categories: non-health system software/digital health solutions, health system operational software, health system clinical software, health & wellness, patient monitoring, care support, digital diagnostics and digital therapeutics.18 To apply these categories to the included studies, some of the definitions were adapted (see table 2 for an adapted version of the definitions):
Initially, the patient monitoring category exclusively monitored specific patient health data. However, in the context of this scoping review, location data were included as well to be able to classify studies that discuss tracking devices.
Only studies explicitly mentioning self-management or similar concepts were assigned to the care support category. Otherwise, nearly all papers would qualify for this category, as they predominantly aim to assist patients in various ways.
The criteria for the digital therapeutics category have been refined and a broader range of digital technologies was encompassed that generate and deliver medical interventions, while the initial definition pertained to ‘health software designed to treat or alleviate a specific disease or medical condition by generating and delivering a medical intervention’.18 It is important to note that not all of them qualify as medical devices or products, and may not be subject to regulation based on the adapted definition for the purpose of this scoping review.
The eight DHT categories with their adapted definitions and examples
| DHT category | Industry and admin-facing | Healthcare providers-facing | Patient-facing | |||||
| Non-health system software/DH solutions | Health system operational software | Health system clinical software | Health & wellness | Patient monitoring | Care support | Digital diagnostics | Digital therapeutics | |
| Overview | Health Information Technology (HIT) and digital health (DH) solutions are designed for stakeholders outside of the hospital and health system settings, such as pharma, medtech, insurances, employers and pharmacies. | Enterprise HIT is aimed at delivering benefits and facilitating support for non-clinical functions (ie, operational and financial aspects). | Enterprise HIT and DH solutions are designed to offer clinicians assistance in effectively managing their patient populations. Divided into four subcategories: clinical documentation & image archiving, communication support, clinical decisions support and telehealth.141 | DH solutions are not specific to any particular disease and primarily focus on capturing and storing general health information while encouraging and facilitating a healthy lifestyle, and do not deliver any medical interventions. | Digital solutions to monitor specific (health or location) data that can be interpreted by a physician to assist in decision making for a medical procedure, or inform a caregiver or family member. | Digital solutions are created to assist patients in improving their self-management of a specific disease or medical condition. May offer recommendations or informations but they do not deliver medical treatments. | Verified digital tools and software that provide a diagnosis or prognosis for a particular disease or medical condition. | Any digital technologies that address or mitigate a disease by generating and implementing a medical intervention. |
| Examples | Accounting Software, documentation tool for compliance. | Software for patient bed management, data & analytics platforms. | Platform for electronic medical records and clinical communications. | Motivational tools, health diaries. | Tracking devices detecting falls, GPS, wearables. | Static care support (medication tracker application), responsive care support. | Apps for dementia screening, algorithm returning a measure of Alzheimer’s disease severity. | Robots, Apps, multimedia systems, Augmented Reality/Virtual Reality. |
Source: DTx Alliance (https://dtxalliance.org).18 141
DH, digital health; DHT, digital health technology; HIT, health information technology.
Patient and public involvement
Patients and/or public were not involved in this research.
Results
Search findings
The search identified 6083 records from the electronic databases. A total of 1056 duplicates were identified and removed, leaving 5027 articles and trial records to be screened. Title and abstract screening led to the exclusion of 4560 records, resulting in 467 full texts that needed to be assessed for eligibility. Of these, 122 studies were included (see online supplemental file 3) and 345 were excluded for the following reasons: 72 did not focus only on PWD or their results could not be separated (wrong population), 69 had the wrong design and 59 pursued wrong aims or outcomes, 42 were the wrong publication type or language (ie, editorials, book chapters, protocols, news articles, non-English), 30 were only trial registry records, 26 could not be accessed, 25 were either pilot, feasibility or usability studies which only tested the viability of intervention, 12 did not deal with QOL and 10 did not include DATs. These are reported in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart in figure 1.
Enlarge this image.Figure 1. PRISMA flow chart. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; QOL, quality of life.
Characteristics of included studies
Scientific literature from 1 January 2013 to 17 May 2023 was included to capture the most recent findings. Figure 2 represents the number of articles published for each year in the date range included for this scoping review.
Enlarge this image.Figure 2. The graph displays the trend of the number of studies published per year.
The authors’ location was collected for each study, ensuring unique country attribution per study while capturing all countries affiliated with each author. There were 180 entries recorded. The countries with the highest number of entries were the UK (n=38, 21.1%), Australia (n=17, 9.4%), the USA (n=14, 7.8%), Canada, China and Norway (n=11, 6.1% each). Notably, European countries showed significant numbers of contributions, including Germany and the Netherlands (n=8, 4.4% each). Sweden and Italy (n=6, 3.3% each) and France (n=4, 2.2%). From the Asian and Oceania continent, contributions were made by Japan (n=6, 3.3%), Taiwan and South Korea (n=5, 2.8% each), New Zealand and Singapore (n=3, 1.7% each), Pakistan and Qatar (n=2, 1.1% each) and India, Indonesia and Malaysia (n=1, 0.6% each). From the African continent, there were only contributions from South Africa (n=4, 2.2%). Some countries, such as Brazil, Cyprus, Czech Republic, Denmark, Greece, Malta, Portugal, Spain and Switzerland, had limited representation, with only 1–2 (0.6%, 1.1%, respectively) contributions each (figure 3).
Enlarge this image.Figure 3. Distribution of the authors’ locations. The countries are coloured based on the number of country attribution, while encompassing all countries associated with each author.
A variety of study designs were used in the included studies (figure 4), comprising of 56 (46%) reviews, 16 (13%) randomised controlled trials (RCTs), 15 (12%) case studies, 13 (11%) intervention studies, 8 (6%) pilot or feasibility studies, 5 (4%) interviews/surveys, 5 (4%) trials, 2 (2%) cohort studies and 2 (2%) observation studies.
Enlarge this image.Figure 4. The distribution of the study designs. RCT, randomised controlled trial.
In total, there were 36 (30%) quantitative studies, 52 (43%) qualitative studies and 34 (28%) that adopted a mixed-methods approach.
The study locations were diverse with 73 different study locations identified. The highest number of studies was carried out in the UK (n=15, 20.5%), followed by Australia (n=9, 12.3%), the USA (n=7, 9.6%), Sweden (n=6, 8.2%) and Norway (n=5, 6.8%). Other locations included Canada, Italy and the Netherlands (n=4, 5.5% each), Denmark and France (n=3, 4.1% each) and Japan and Taiwan (n=2, 2.7% each). There was one study (1.4%) each from China, Cyprus, Germany, Ghana, Greece, Korea, New Zealand, Singapore and Spain. Within these locations, different study settings were used; home-based (n=25, 33.8%), residential care (n=10, 13.5%), long-term care and nursing homes (n=9, 12.2% each), daycare centres (n=7, 9.5%), community-based and hospital (n=4, 5.4% each), other care facilities or memory clinics (n=3, 4.1% each) and a laboratory setting (n=1, 1.4%).
Diverse target groups of the included studies were identified, as presented in table 3. As indicated in the review process and as part of the exclusion criteria, only research articles that could separate results for PWD from other comorbidities or conditions were included.
Table 3The different target groups of the included studies sorted by the number of studies
| Target population of the included studies | Number of studies (%) | References |
| PWD | 72 (59) | 8 21–27 45–59 66–77 84–105 115 116 120–123 126–132 137 142 |
| PWD and caregivers | 31 (25.4) | 19 28–39 60–63 78–80 106–108 114 117 118 124 133 138 143 |
| PWD and MCI patients | 4 (3.3) | 20 40 41 119 |
| PWD and healthy adults | 4 (3.3) | 81 109 134 135 |
| PWD, caregiver and stakeholders | 3 (2.5) | 42 64 110 |
| Diverse stakeholders | 2 (1.6) | 82 83 |
| PWD with chronic pain | 1 (0.8) | 111 |
| PWD, MCI patients and cognitive impairments | 1 (0.8) | 112 |
| PWD, MCI patients and caregivers | 1 (0.8) | 43 |
| PWD, MCI patients and healthy adults | 1 (0.8) | 65 |
| PWD and psychiatric disorders | 1 (0.8) | 113 |
| PWD and Parkinson’s disease patients | 1 (0.8) | 136 |
The frequency of each class is indicated (as the number of studies), along with the corresponding references.
MCI, mild cognitive impairment; PWD, people with dementia.
Characteristics of DATs
Categories of DATs
The DATs of the 122 included studies underwent classification into the DHT categories provided by the DTx Alliance.18 Some studies discussed different DATs and therefore were assigned to multiple categories due to their varying applicability (see table 4).
Table 4The different classes of DATs
| DHT category | Number of assigned DATs (%) | References |
| Non-health system software/DH solutions | 0 | – |
| Health system operational software | 0 | – |
| Health system clinical software | 1 (0.7) | 114 |
| Health & wellness | 0 | – |
| Patient monitoring | 30 (20.8) | 19–33 35–44 46 88 90 137 138 |
| Care support | 2 (1.4) | 30 45 |
| Digital diagnostics | 2 (1.4) | 30 46 |
| Digital therapeutics | 109 (75.7) | 8 21–23 25–31 33 35 37 40 41 44–75 77–113 115–124 126–136 138 142 143 |
| Total | 144 |
The frequency of each category is indicated, along with the corresponding references.
DATs, digital assistive technologies; DH, digital health; DHT, digital health technology.
Qualitative aspects of QOL
Positive impact of DATs on QOL dimensions
Through qualitative analysis of the included studies, recurring themes were identified that reflect the different aspects of how DATs influence QOL for PWD: preserving autonomy, engagement and social interaction, health monitoring and promotion, improving activities of daily living (ADL), improving cognition, maintaining dignity, managing behavioural and psychological symptoms of dementia (BPSD) and safety/surveillance (online supplemental file 4). These concepts can influence each other, and it is possible for a single DAT to simultaneously have multiple effects. In the following sections, these impacts will be discussed in more detail based on the analysis of the included studies.
Preserving autonomy
Autonomy can be understood as an individual’s ability to be involved in decision making, consent, receiving treatment or intervention (ie, to choose and act independently without coercion).17 Within healthcare, the preservation of autonomy has emerged as a significant topic, most importantly known as the principle of respect for autonomy. DATs can be seen as a useful tool for promoting autonomy and, therefore, positively contributing to PWD’s lives. The use of DATs is intended to increase the capacity of an individual to participate in decision making19 20 and promote independent living, and enable ageing-in-place.21
From 50 of the included studies, the following were used to promote autonomy: patient monitoring,19–44 care support30 45 and digital therapeutics.8 21–23 25–31 33 35 37 40 41 44–65
Engagement and social interaction
The topic of engagement and social interaction was highlighted in 64 studies. They differentiated between cases where DATs increased social interaction23 31 40 54–56 58 64 66–83 and fostered higher engagement (ie, in therapy settings).8 20 23 31 44 45 53 55 61 63–65 68 69 71–73 77 79 82 84–113
Sixty-two of the included studies demonstrated that digital therapeutics play a significant role in promoting social interaction and engagement,8 23 31 40 44 45 53–56 58 61 63–75 77–113 including other DATs from the patient monitoring40 and care support categories.30 45
Health monitoring and promotion
Health monitoring refers to monitoring bodily functions and reporting their states or imbalances. Ten studies reported using DATs to monitor and promote health for PWD.21 23 30 32 37 40 41 46 56 114 This was achieved, for example, through social assistive robots56 and wearables,32 including wristwatches that measure movement, skin temperature and pulses.37
Improving ADL
DATs support PWD in maintaining or regaining their ability to perform ADL such as meal preparation, managing medication or communication. Forty-five of the included studies highlight this field’s broad spectrum of possibilities, for instance, they range from the implementation of interventions from the patient monitoring category,20 24 26 29 31 33 34 40 43 care support,30 45 to digital therapeutics.8 22 25 26 28–31 33 35 37 40 45–53 55–63 65 86 94 97 103 107 115–119
Improving cognition
The varied application of DATs can enhance cognitive abilities, as indicated by 34 studies. They demonstrated that patient monitoring,43 care support30 45 and digital therapeutics21–23 26–28 30 31 40 45 46 53–55 65 71 75 83–85 90 95 98 103 115–117 119–124 can lead to improvements in cognitive functioning.
Maintaining dignity
In contrast to autonomy, dignity refers to the ability to preserve self-respect and personhood, while being recognised and valued in society, and discussed in 13 studies using diverse DAT categories: digital therapeutics21 25 35 41 and patient monitoring.8 21 22 25 35 41 56 58 62 77 78 81 108 These categories are specifically designed to uphold and promote ageing in place, as demonstrated in the review of Gettel et al,21 by the use of pet robots to maintain the resident’s dignity and self-worth.78
Managing BPSD
There are 12 BPSD, which include aberrant motor behaviour, agitation, anxiety, apathy, appetite changes, delusions, depression, disinhibition, elation/euphoria, hallucinations, irritability and sleep changes.125 Different BPSD were shown to be managed by DATs in 64 studies through digital therapeutics8 23 26–28 30 40 41 48 55 60 67–72 75 77 78 81–86 88 89 91–93 95 97–102 104 105 107 109 111–113 115–117 120 122–124 126–136 or by patient monitoring devices.30 48 69 86 93 98 133 All the DATS were reported to have a positive effect on BPSD.
Safety/surveillance
Thirty-four of the included studies demonstrated that DATs can enhance the safety of PWD, for instance through patient monitoring,19–31 33–44 46 88 137 138 care support30 and digital therapeutics.21 23 25–27 29–31 37 44 49 53 56 62 71 138
Negative impacts of DATs on QOL dimensions
Among the included studies, 18 studies reported negative impacts of DATs on the QOL.8 25 38 41 53 55 56 60 61 63 65 81 97 101 104 120 127 130 These negative impacts included increased anxiety,8 81 97 worsened agitation,127 decreased interactions with caregivers,55 confusion,53 worsened BPSD,31 53 127 increased hallucinations and decreased mood,53 anxiety towards the technology,8 75 81 as well as aggression, rejection or disliking the technology.81
Instruments measuring the QOL
As demonstrated, DATs can diversely impact aspects of QOL. It was observed that numerous studies employ specific measures to assess these impacts, which can manifest in various forms. During the charting process, 33 studies revealed the utilisation of different units of measurement in this context. In 18 studies, the Quality of Life in Alzheimer’s Disease Scale (QOL-AD) was used,8 22 41 48 55 67 71 77 92 95 97 105 110 112 115 122 127 130 and in 11 studies, the Quality of Life in Late-Stage Dementia scale (QUALID).8 27 60 70 71 77 97 112 126 127 134 Additionally, the self-reported Dementia Quality of Life measure (DEMQoL) was employed in 4 studies,60 79 97 114 the Dementia Quality of Life Instrument (DQoL) in 3 studies27 28 71 and the EQ-5D-5L in 3 studies.60 62 124 Other measuring instruments were used only once in each case: Quality of Life (GQL8),119 Quality of Life for People with Dementia (QUALIDEM),71 Quality of Life Alzheimer’s disease (QoL) scale,117 SF-36 quality of life instrument,32 Cantril QoL ladder,93 Carer-Qol-7D,79 EUROHIS-QoL-8 and EuroQoL 5 Dimension Questionnaire.8
Discussion
This scoping review and synthesis of 122 studies provided a detailed mapping of the different kinds of DATs available which target QOL for PWD, and the diverse study designs contributing to the research community. This is particularly useful for future researchers to identify whether certain designs (ie, reviews, n=56) have been more extensively explored than others (ie, RCTs, n=16; case studies, n=15), encouraging more empirical evidences to be reached in order to truly understand the effects of DATs in improving the QOL of PWD.
DATs were categorised into the DTx Alliance categories, with the largest category being digital therapeutics (n=109), followed by patient monitoring (n=30), digital diagnostics (n=2), care support (n=2) and health system clinical software (n=1). This distribution can be attributed to the search strategy, which specifically targeted DATs with a therapeutic focus.
This review highlights that DATs have the potential to impact the QOL of PWD in several identified thematic areas: preserving autonomy, engagement, social interaction, health monitoring and promotion, improving ADL, cognition, maintaining dignity, managing BPSD and safety/surveillance. The different categories of DATs can provide diverse forms of support to PWD across these thematic areas, enhancing their overall QOL. DATs help PWD to age in place, live independently and maintain their dignity, especially when supported by DATs that help PWD keep or regain their ability to engage in daily activities. DATs were also shown to help promote health by tracking bodily functions and encouraging engagement and social contact. Overall, impacts could be seen on individuals’ cognitive abilities, to manage BPSD, and improvements to their general well-being and safety.
However, in a few instances, negative impacts of DATs on the QOL of PWD were also identified.53 55 81 97 127 For example, worsened BPSD, worries about negative results, anxiety and aggression towards the technology, or disliking the technology.
In addition to the qualitative impacts, various quantitative quality-of-life instruments were examined. It was found that 12 different instruments were used across 18 of the included studies. The most frequently employed instrument was the Quality of Life in Alzheimer’s Disease Scale (QOL-AD). Furthermore, it was observed that DATs are used to support therapy,27 and can serve as therapeutic tools to even constitute a distinct form of therapy in themselves.84
Strengths and limitations
This scoping review first attempted to provide an extensive overview of various DATs and their impact on the QOL for PWD. A comprehensive search strategy was implemented to achieve this, covering five electronic databases over a decade. However, considering the rapidly expanding landscape of DATs and the exclusion of conference proceedings, book chapters, pilot and feasibility studies, it is conceivable that this scoping review might overlook ongoing or planned studies that could shed light on alternative DATs or different impacts on QOL. Additionally, concepts of autonomy and dignity were not incorporated in the initial search strategy as their prominence in relevant literature was only discovered by authors during the coding stage. Therefore, there is a possibility that this could have led to some missed studies relating to these concepts. Further, it should also be pointed out that while ‘accessible DATS’ were not further defined in the objectives of this study, we used the terms colloquially to imply that there might be solutions that cannot be accessed by our search strategy.
Conducting a scoping review instead of a systematic review has its limitations. In this approach, an assessment of the quality of the included studies or an evaluation of intervention effectiveness needs to be incorporated. Therefore, the validity of interventions on their measured outcomes may present bias or need to be better analysed and reported in included studies. The search strategy prioritised patient-facing DATs, thereby introducing a potential bias that elucidates the high predominance of digital therapeutics in the included literature. Another limitation of this work arises from the challenge of providing a conclusive quantitative assessment of the effects of DATs on QOL. While the primary objective of our work was to offer a comprehensive and broad understanding of the subject, we envision that this scoping review can lay the groundwork for subsequent systematic reviews and meta-analyses, which would employ specific research questions and analytical strategies for dedicated quantitative evaluations.
Nonetheless, due to the restricted availability of evaluative research in this domain, the primary objective was to explore the impact of DATs on the QOL for PWD, which this study has provided.
Conclusion
A variety of DATs are available, offering versatile applications and holding the potential to serve as promising instruments for enhancing the QOL among PWD. Further research must be conducted to examine the ongoing developments in DATs and their increasing impact on QOL, including their long-term effects, and a deeper conceptual understanding of how certain interventions correlate to improving QOL. Moreover, future research could consider placing a particular emphasis on the less-represented DHT categories, such as care support, health & wellness or software. Digital innovations offer significant potential in addressing the global increase in the elderly population and revolutionising various aspects of elderly care.4 Considering future research directions, for example, in the context of voice assistants and advancements in large language models,139 140 would be of importance, as they facilitate the development of an interface that is more intuitive and natural, without demanding a high degree of dexterity.
Data availability statement
Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
Not applicable.
Contributors All authors have made substantial intellectual contributions to developing this scoping review and its revisions. The search question was conceptualised by TK and RV, and further developed by CS. The review approach and design were conceptualised by RV, with advice from TK. CS and RV developed and tested search terms with input and revisions from TK. CS and RV jointly screened all the studies resulting from the search strategy. CS conducted the full-text data extraction, with RV, MN and TK reviewing and providing consultation. CS drafted the first manuscript, and all authors were involved in the revision of the manuscript. All authors approved the final version of the manuscript. TK is the author responsible for the overall content as the guarantor.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
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Competing interests MN and TK are affiliated with the Centre for Digital Health Interventions (CDHI), a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich and the Institute of Technology Management and School of Medicine at the University of St. Gallen. CDHI is funded in part by CSS, a Swiss health insurer. TK is also a co-founder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither CSS nor Pathmate Technologies was involved in this research. All other authors declare no conflict of interest.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
1 World Health Organization. Dementia. 2023. Available: https://www.who.int/news-room/fact-sheets/detail/dementia#:%7E:text=Worldwide%2C%20around%2050%20million%20people,dependency%20among%20older%20people%20worldwide [Accessed 15 May 2023 ].
2 Koo BM, Vizer LM. Examining mobile technologies to support older adults with dementia through the lens of personhood and human needs: scoping review. JMIR Mhealth Uhealth 2019; 7: e15122. doi:10.2196/15122
3 Association for the Advancement of Assistive Technology in Europe. Available: www.aaate.net
4 Chen C, Ding S, Wang J. Digital health for aging populations. Nat Med 2023; 29: 1623–30. doi:10.1038/s41591-023-02391-8
5 Ehn M, Richardson MX, Landerdahl Stridsberg S, et al. Mobile safety alarms based on GPS technology in the care of older adults: systematic review of evidence based on a general evidence framework for digital health technologies. J Med Internet Res 2021; 23: e27267. doi:10.2196/27267
6 Kelly PA, Cox LA, Petersen SF, et al. The effect of PARO robotic seals for hospitalized patients with dementia: a feasibility study. Geriatric Nursing 2021; 42: 37–45. doi:10.1016/j.gerinurse.2020.11.003
7 Lee H, Chung MA, Kim H, et al. The effect of cognitive function health care using artificial intelligence robots for older adults: systematic review and meta-analysis. JMIR Aging 2022; 5: e38896. doi:10.2196/38896
8 Appel L, Ali S, Narag T, et al. Virtual reality to promote wellbeing in persons with dementia: a scoping review. J Rehabil Assist Technol Eng 2021; 8: 20556683211053952. doi:10.1177/20556683211053952
9 De Vito AN, Sawyer RJ, LaRoche A, et al. Acceptability and feasibility of a multicomponent telehealth care management program in older adults with advanced dementia in a residential memory care unit. Gerontol Geriatr Med 2020; 6: 2333721420924988. doi:10.1177/2333721420924988
10 Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005; 8: 19–32. doi:10.1080/1364557032000119616
11 Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169: 467–73. doi:10.7326/M18-0850
12 Schneider C, Kowatsch T, Vinay R. Impact of digital assistive technologies on the quality of life for people with dementia: protocol for a scoping review. BMJ Open 2023; 13: e077017. doi:10.1136/bmjopen-2023-077017
13 Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101. doi:10.1191/1478088706qp063oa
14 Burckhardt CS, Anderson KL. The quality of life scale (QOLS): reliability, validity, and utilization quality of life scaleqolschronic illness outcomesquality of life evaluation why assess quality of life in chronic illness? What does the quality of life scale (QOLS) measure? 2003. Available: http://www.hqlo.com/content/1/1/60
15 Wolak-Thierry A, Novella JL, Barbe C, et al. Comparison of QoL-AD and DQoL in elderly with Alzheimer’s disease. Aging Ment Health 2015; 19: 274–8. doi:10.1080/13607863.2014.927822
16 Jing W, Willis R, Feng Z. Factors influencing quality of life of elderly people with dementia and care implications: a systematic review. Arch Gerontol Geriatr 2016; 66: 23–41. doi:10.1016/j.archger.2016.04.009
17 Beauchamp TL, Childress JF. Principles of Biomedical Ethics, 6th ed. New York: Oxford University Press, 2009.
18 DTx Alliance. Digital health technology ecosystem categorization what are digital health technologies? n.d. Available: www.dtxalliance.org
19 Magnusson L, Sandman L, Rosén KG, et al. Extended safety and support systems for people with dementia living at home. J Assist Technol 2014; 8: 188–206. doi:10.1108/JAT-10-2014-0021
20 Lazarou I, Karakostas A, Stavropoulos TG, et al. A novel and intelligent home monitoring system for care support of elders with cognitive impairment. J Alzheimers Dis 2016; 54: 1561–91. doi:10.3233/JAD-160348
21 Gettel CJ, Chen K, Goldberg EM. Dementia care, fall detection, and ambient-assisted living technologies help older adults age in place: a scoping review. J Appl Gerontol 2021; 40: 1893–902. doi:10.1177/07334648211005868
22 Daly Lynn J, Rondón-Sulbarán J, Quinn E, et al. A systematic review of electronic assistive technology within supporting living environments for people with dementia. Dementia 2019; 18: 2371–435. doi:10.1177/1471301217733649
23 Lima MR, Wairagkar M, Gupta M, et al. Conversational affective social robots for ageing and dementia support. IEEE Trans Cogn Dev Syst 2022; 14: 1378–97. doi:10.1109/TCDS.2021.3115228
24 Olsson A, Engström M, Åsenlöf P, et al. Effects of tracking technology on daily life of persons with dementia: three experimental single-case studies. Am J Alzheimers Dis Other Demen 2015; 30: 29–40. doi:10.1177/1533317514531441
25 Löbe C, AboJabel H. Empowering people with dementia via using intelligent assistive technology: a scoping review. Arch Gerontol Geriatr 2022; 101: 104699. doi:10.1016/j.archger.2022.104699
26 Kruse CS, Fohn J, Umunnakwe G, et al. Evaluating the facilitators, barriers, and medical outcomes commensurate with the use of assistive technology to support people with dementia: a systematic review literature. Healthcare (Basel) 2020; 8: 278. doi:10.3390/healthcare8030278
27 Sanders D, Scott P. Literature review: technological interventions and their impact on quality of life for people living with dementia. BMJ Health Care Inform 2020; 27: e100064. doi:10.1136/bmjhci-2019-100064
28 Pappadà A, Chattat R, Chirico I, et al. Assistive technologies in dementia care: an updated analysis of the literature. Front Psychol 2021; 12: 644587. doi:10.3389/fpsyg.2021.644587
29 Warner L, Tipping LR. Can everyday assistive technologies provide meaningful support to persons with dementia and their informal caregivers? Evaluation of collaborative community program. J Appl Gerontol 2022; 41: 2022–32. doi:10.1177/07334648221101041
30 Koumakis L, Chatzaki C, Kazantzaki E, et al. Dementia care frameworks and assistive technologies for their implementation: a review. IEEE Rev Biomed Eng 2019; 12: 4–18. doi:10.1109/RBME.2019.2892614
31 Klimova B, Valis M, Kuca K. Exploring assistive technology as a potential beneficial intervention tool for people with Alzheimer’s disease - a systematic review. Neuropsychiatr Dis Treat 2018; 14: 3151–8. doi:10.2147/NDT.S181849
32 Larnyo E, Dai B, Larnyo A, et al. Impact of actual use behavior of healthcare wearable devices on quality of life: a cross-sectional survey of people with dementia and their caregivers in Ghana. Healthcare (Basel) 2022; 10: 275. doi:10.3390/healthcare10020275
33 D’Onofrio G, Sancarlo D, Ricciardi F, et al. Information and communication technologies for the activities of daily living in older patients with dementia: a systematic review. J Alzheimers Dis 2017; 57: 927–35. doi:10.3233/JAD-161145
34 Øderud T, Landmark B, Eriksen S, et al. Persons with dementia and their caregivers using GPS. Stud Health Technol Inform 2015; 217: 212–21. doi:10.3233/978-1-61499-566-1-212
35 Lariviere M, Poland F, Woolham J, et al. Placing assistive technology and telecare in everyday practices of people with dementia and their caregivers: findings from an embedded ethnography of a national dementia trial. BMC Geriatr 2021; 21: 121. doi:10.1186/s12877-020-01896-y
36 Fowler-Davis S, Barnett D, Kelley J, et al. Potential for digital monitoring to enhance wellbeing at home for people with mild dementia and their family carers. J Alzheimers Dis 2020; 73: 867–72. doi:10.3233/JAD-190844
37 Behera CK, Condell J, Dora S, et al. State-of-the-art sensors for remote care of people with dementia during a pandemic: a systematic review. Sensors 2021; 21: 4688. doi:10.3390/s21144688
38 Neubauer NA, Lapierre N, Ríos-Rincón A, et al. What do we know about technologies for dementia-related wandering? A scoping review. Can J Occup Ther 2018; 85: 196–208. doi:10.1177/0008417418777530
39 Olsson A, Engström M, Lampic C, et al. A passive positioning alarm used by persons with dementia and their spouses--A qualitative intervention study. BMC Geriatr 2013; 13: 11. doi:10.1186/1471-2318-13-11
40 Piau A, Campo E, Rumeau P, et al. Aging society and gerontechnology: a solution for an independent living? J Nutr Health Aging 2014; 18: 97–112. doi:10.1007/s12603-013-0356-5
41 Holthe T, Halvorsrud L, Lund A. Digital assistive technology to support everyday living in community-dwelling older adults with mild cognitive impairment and dementia. Clin Interv Aging 2022; 17: 519–44. doi:10.2147/CIA.S357860
42 Cooper J, Burrow S, Pusey H. What are the perceptions of people living with dementia, family carers, professionals and other potential stakeholders to the use of global positioning systems to promote safer outdoor walking?: a qualitative literature review. Disabil Rehabil Assist Technol 2021; 16: 614–23. doi:10.1080/17483107.2019.1686074
43 Moyle W, Murfield J, Lion K. The effectiveness of smart home technologies to support the health outcomes of community-dwelling older adults living with dementia: a scoping review. Int J Med Inform 2021; 153: 104513. doi:10.1016/j.ijmedinf.2021.104513
44 Daly-Lynn J, Ryan A, McCormack B, et al. Stakeholder’s experiences of living and caring in technology-rich supported living environments for tenants living with dementia. BMC Geriatr 2023; 23: 62. doi:10.1186/s12877-023-03751-2
45 Lee AR, Gerritzen EV, McDermott O, et al. Exploring the role of web-based interventions in the self-management of dementia: systematic review and narrative synthesis. J Med Internet Res 2021; 23: e26551. doi:10.2196/26551
46 Yousaf K, Mehmood Z, Awan IA, et al. A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD). Health Care Manag Sci 2020; 23: 287–309. doi:10.1007/s10729-019-09486-0
47 Perilli V, Lancioni GE, Laporta D, et al. A computer-aided telephone system to enable five persons with Alzheimer’s disease to make phone calls independently. Res Dev Disabil 2013; 34: 1991–7. doi:10.1016/j.ridd.2013.03.016
48 Cunningham S, Brill M, Whalley JH, et al. Assessing wellbeing in people living with dementia using reminiscence music with a mobile app (memory tracks): a mixed methods cohort study. J Healthc Eng 2019; 2019: 8924273. doi:10.1155/2019/8924273
49 Berrett J, de Kruiff A, Pedell S, et al. Augmented assistive technology: the importance of tailoring technology solutions for people living with dementia at home. International Journal of Human-Computer Studies 2022; 165: 102852. doi:10.1016/j.ijhcs.2022.102852
50 Imbeault H, Langlois F, Bocti C, et al. Can people with Alzheimer’s disease improve their day-to-day functioning with a tablet computer? Neuropsychol Rehabil 2018; 28: 779–96. doi:10.1080/09602011.2015.1133431
51 Imbeault H, Bier N, Pigot H, et al. Electronic organiser and Alzheimer’s disease: fact or fiction? Neuropsychol Rehabil 2014; 24: 71–100. doi:10.1080/09602011.2013.858641
52 Imbeault H, Gagnon L, Pigot H, et al. Impact of AP@LZ in the daily life of three persons with Alzheimer’s disease: long-term use and further exploration of its effectiveness. Neuropsychol Rehabil 2018; 28: 755–78. doi:10.1080/09602011.2016.1172491
53 Flynn A, Healy D, Barry M, et al. Key stakeholders’ experiences and perceptions of virtual reality for older adults living with dementia: systematic review and thematic synthesis. JMIR Serious Games 2022; 10: e37228. doi:10.2196/37228
54 Cheng Z, Zhou M, Sabran K. Mobile app-based interventions to improve the well-being of people with dementia: a systematic literature review. Assist Technol 2024; 36: 64–74. doi:10.1080/10400435.2023.2206439
55 Ghafurian M, Hoey J, Dautenhahn K. Social robots for the care of persons with dementia. J Hum-Robot Interact 2021; 10: 1–31. doi:10.1145/3469653
56 Fardeau E, Senghor AS, Racine E. The impact of socially assistive robots on human flourishing in the context of dementia: a scoping review. Int J Soc Robot 2023: 1–51. doi:10.1007/s12369-023-00980-8
57 Foloppe DA, Richard P, Yamaguchi T, et al. The potential of virtual reality-based training to enhance the functional autonomy of Alzheimer’s disease patients in cooking activities: a single case study. Neuropsychol Rehabil 2018; 28: 709–33. doi:10.1080/09602011.2015.1094394
58 Talbot CV, Briggs P. The use of digital technologies by people with mild-to-moderate dementia during the COVID-19 pandemic: a positive technology perspective. Dementia (London) 2022; 21: 1363–80. doi:10.1177/14713012221079477
59 Tellier M, Auger C, Bier N, et al. Use of an electronic pillbox by older adults with mild Alzheimer’s disease: impact on medication administration and adherence. Gerontechnology 2020; 19: 66–76. doi:10.4017/gt.2020.19.1.007.00
60 Leroi I, Simkin Z, Hooper E, et al. Impact of an intervention to support hearing and vision in dementia: the SENSE-Cog field trial. Int J Geriatr Psychiatry 2020; 35: 348–57. doi:10.1002/gps.5231
61 Wang RH, Sudhama A, Begum M, et al. Robots to assist daily activities: views of older adults with Alzheimer’s disease and their caregivers. Int Psychogeriatr 2017; 29: 67–79. doi:10.1017/S1041610216001435
62 Amabili G, Cucchieri G, Margaritini A, et al. Social robotics and dementia: results from the eWare project in supporting older people and their informal caregivers. Int J Environ Res Public Health 2022; 19: 20. doi:10.3390/ijerph192013334
63 Evans N, Boyd H, Harris N, et al. The experience of using prompting technology from the perspective of people with dementia and their primary carers. Aging Ment Health 2021; 25: 1433–41. doi:10.1080/13607863.2020.1745145
64 Casey D, Barrett E, Kovacic T, et al. The perceptions of people with dementia and key stakeholders regarding the use and impact of the social robot MARIO. Int J Environ Res Public Health 2020; 17: 1–19. doi:10.3390/ijerph17228621
65 Góngora Alonso S, Hamrioui S, de la Torre Díez I, et al. Social robots for people with aging and dementia: a systematic review of literature. Telemed J E Health 2019; 25: 533–40. doi:10.1089/tmj.2018.0051
66 Su Z, Bentley BL, McDonnell D, et al. 6G and artificial intelligence technologies for dementia care: literature review and practical analysis. J Med Internet Res 2022; 24: e30503. doi:10.2196/30503
67 Shoesmith E, Surr C, Ratschen E. Animal-assisted and robotic animal-assisted interventions within dementia care: a systematic review. Dementia (London) 2023; 22: 664–93. doi:10.1177/14713012231155985
68 Aarskog NK, Hunskår I, Bruvik F. Animal-assisted interventions with dogs and robotic animals for residents with dementia in nursing homes: a systematic review. Phys Occup Ther Geriatr 2019; 37: 77–93. doi:10.1080/02703181.2019.1613466
69 Budak KB, Atefi G, Hoel V, et al. Can technology impact loneliness in dementia? A scoping review on the role of assistive technologies in delivering psychosocial interventions in long-term care. Disabil Rehabil Assist Technol 2023; 18: 1107–19. doi:10.1080/17483107.2021.1984594
70 Jøranson N, Pedersen I, Rokstad AMM, et al. Change in quality of life in older people with dementia participating in Paro-activity: a cluster-randomized controlled trial. J Adv Nurs 2016; 72: 3020–33. doi:10.1111/jan.13076
71 Rai HK, Kernaghan D, Schoonmade L, et al. Digital technologies to prevent social isolation and loneliness in dementia: a systematic review. JAD 2022; 90: 513–28. doi:10.3233/JAD-220438
72 Feng Y, Barakova EI, Yu S, et al. Effects of the level of interactivity of a social robot and the response of the augmented reality display in contextual interactions of people with dementia. Sensors 2020; 20: 3771. doi:10.3390/s20133771
73 Jøranson N, Pedersen I, Rokstad AMM, et al. Group activity with Paro in nursing homes: systematic investigation of behaviors in participants. Int Psychogeriatr 2016; 28: 1345–54. doi:10.1017/S1041610216000120
74 Samuelsson C, Ferm U, Ekström A. “It’s Our Gang” - Promoting social inclusion for people with dementia by using digital communication support in a group activity. Clin Gerontol 2021; 44: 418–29. doi:10.1080/07317115.2020.1795037
75 Hsieh C-J, Li P-S, Wang C-H, et al. Socially assistive robots for people living with dementia in long-term facilities: a systematic review and meta-analysis of randomized controlled trials. Gerontology 2023; 69: 1027–42. doi:10.1159/000529849
76 Ambegaonkar A, Ritchie C, de la Fuente Garcia S. The use of mobile applications as communication aids for people with dementia: opportunities and limitations. J Alzheimers Dis Rep 2021; 5: 681–92. doi:10.3233/ADR-200259
77 Neal I, du Toit SHJ, Lovarini M. The use of technology to promote meaningful engagement for adults with dementia in residential aged care: a scoping review. Int Psychogeriatr 2020; 32: 913–35. doi:10.1017/S1041610219001388
78 Scerri A, Sammut R, Scerri C. Formal caregivers’ perceptions and experiences of using pet robots for persons living with dementia in long-term care: a meta-ethnography. J Adv Nurs 2021; 77: 83–97. doi:10.1111/jan.14581
79 Hoel V, Ambugo EA, Wolf-Ostermann K. Sustaining our relationship: dyadic interactions supported by technology for people with dementia and their informal caregivers. IJERPH 2022; 19: 10956. doi:10.3390/ijerph191710956
80 Hoel V, Feunou CM, Wolf-Ostermann K. Technology-driven solutions to prompt conversation, aid communication and support interaction for people with dementia and their caregivers: a systematic literature review. BMC Geriatr 2021; 21: 157. doi:10.1186/s12877-021-02105-0
81 Koh WQ, Ang FXH, Casey D. Impacts of low-cost robotic pets for older adults and people with dementia: scoping review. JMIR Rehabil Assist Technol 2021; 8: e25340. doi:10.2196/25340
82 Moyle W, Bramble M, Jones C, et al. Care staff perceptions of a social robot called Paro and a look-alike Plush Toy: a descriptive qualitative approach. Aging Ment Health 2018; 22: 330–5. doi:10.1080/13607863.2016.1262820
83 Moyle W, Bramble M, Jones CJ, et al. “She had a smile on her face as wide as the great Australian bite”: a qualitative examination of family perceptions of a therapeutic robot and a Plush toy. Gerontologist 2019; 59: 177–85. doi:10.1093/geront/gnx180
84 D’Cunha NM, Nguyen D, Naumovski N, et al. A mini-review of virtual reality-based interventions to promote well-being for people living with dementia and mild cognitive impairment. Gerontology 2019; 65: 430–40. doi:10.1159/000500040
85 Rouaix N, Retru-Chavastel L, Rigaud A-S, et al. Affective and engagement issues in the conception and assessment of a robot-assisted psychomotor therapy for persons with dementia. Front Psychol 2017; 8: 950. doi:10.3389/fpsyg.2017.00950
86 Leng FY, Yeo D, George S, et al. Comparison of iPad applications with traditional activities using person-centred care approach: impact on well-being for persons with dementia. Dementia (London) 2014; 13: 265–73. doi:10.1177/1471301213494514
87 Feng Y, Perugia G, Yu S, et al. Context-enhanced human-robot interaction: exploring the role of system interactivity and multimodal stimuli on the engagement of people with dementia. Int J of Soc Robotics 2022; 14: 807–26. doi:10.1007/s12369-021-00823-4
88 Chan DKY, Chan LKM, Kuang YM, et al. Digital care technologies in people with dementia living in long-term care facilities to prevent falls and manage behavioural and psychological symptoms of dementia: a systematic review. Eur J Ageing 2022; 19: 309–23. doi:10.1007/s10433-021-00627-5
89 Moyle W, Jones C, Murfield J, et al. Effect of a robotic seal on the motor activity and sleep patterns of older people with dementia, as measured by wearable technology: a cluster-randomised controlled trial. Maturitas 2018; 110: 10–7. doi:10.1016/j.maturitas.2018.01.007
90 Strong J. Immersive virtual reality and persons with dementia: a literature review. J Gerontol Soc Work 2020; 63: 209–26. doi:10.1080/01634372.2020.1733726
91 Bradwell H, Edwards KJ, Winnington R, et al. Implementing affordable socially assistive pet robots in care homes before and during the COVID-19 pandemic: stratified cluster randomized controlled trial and mixed methods study. JMIR Aging 2022; 5: e38864. doi:10.2196/38864
92 D’Onofrio G, Sancarlo D, Raciti M, et al. MARIO project: validation and evidence of service robots for older people with dementia. J Alzheimers Dis 2019; 68: 1587–601. doi:10.3233/JAD-181165
93 Yu F, Mathiason MA, Johnson K, et al. Memory matters in dementia: efficacy of a mobile reminiscing therapy app. Alzheimers Dement (N Y) 2019; 5: 644–51. doi:10.1016/j.trci.2019.09.002
94 Zarit SH, Chiusano C, Harrison AS, et al. Rehabilitation of persons with dementia: using technology to improve participation. Aging Ment Health 2021; 25: 543–50. doi:10.1080/13607863.2020.1711864
95 Kang HS, Makimoto K, Konno R, et al. Review of outcome measures in PARO robot intervention studies for dementia care. Geriatr Nurs 2020; 41: 207–14. doi:10.1016/j.gerinurse.2019.09.003
96 Chu MT, Khosla R, Khaksar SMS, et al. Service innovation through social robot engagement to improve dementia care quality. Assist Technol 2017; 29: 8–18. doi:10.1080/10400435.2016.1171807
97 Yu C, Sommerlad A, Sakure L, et al. Socially assistive robots for people with dementia: systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life. Ageing Res Rev 2022; 78: 101633. doi:10.1016/j.arr.2022.101633
98 Moon S, Park K. The effect of digital reminiscence therapy on people with dementia: a pilot randomized controlled trial. BMC Geriatr 2020; 20: 166. doi:10.1186/s12877-020-01563-2
99 Cho E, Shin J, Seok JW, et al. The effectiveness of non-pharmacological interventions using information and communication technologies for behavioral and psychological symptoms of dementia: a systematic review and meta-analysis. Int J Nurs Stud 2023; 138: 104392. doi:10.1016/j.ijnurstu.2022.104392
100 Chen SC, Jones C, Moyle W. The impact of engagement with the PARO therapeutic robot on the psychological benefits of older adults with dementia. Clin Gerontol 2022: 1–13. doi:10.1080/07317115.2022.2117674
101 Fogelson DM, Rutledge C, Zimbro KS. The impact of robotic companion pets on depression and loneliness for older adults with dementia during the COVID-19 pandemic. J Holist Nurs 2022; 40: 397–409. doi:10.1177/08980101211064605
102 Goodall G, Taraldsen K, Serrano JA. The use of technology in creating individualized, meaningful activities for people living with dementia: a systematic review. Dementia (London) 2021; 20: 1442–69. doi:10.1177/1471301220928168
103 Hitch D, Swan J, Pattison R, et al. Use of touchscreen tablet technology by people with dementia in homes: a scoping review. J Rehabil Assist Technol Eng 2017; 4: 2055668317733382. doi:10.1177/2055668317733382
104 Moyle W, Jones C, Murfield J, et al. Using a therapeutic companion robot for dementia symptoms in long-term care: reflections from a cluster-RCT. Aging Ment Health 2019; 23: 329–36. doi:10.1080/13607863.2017.1421617
105 Hung L, Chow B, Shadarevian J, et al. Using touchscreen tablets to support social connections and reduce responsive behaviours among people with dementia in care settings: a scoping review. Dementia (London) 2021; 20: 1124–43. doi:10.1177/1471301220922745
106 Samuelsson C, Ekström A. Digital communication support in interaction involving people with dementia. Logoped Phoniatr Vocol 2019; 44: 41–50. doi:10.1080/14015439.2019.1554856
107 Tyack C, Camic PM. Touchscreen interventions and the well-being of people with dementia and caregivers: a systematic review. Int Psychogeriatr 2017; 29: 1261–80. doi:10.1017/S1041610217000667
108 Paay J, Kjeldskov J, Aaen I, et al. User-centred iterative design of a smartwatch system supporting spontaneous reminiscence therapy for people living with dementia. Health Informatics J 2022; 28: 146045822211060. doi:10.1177/14604582221106002
109 Fields N, Xu L, Greer J, et al. Shall I compare thee…to a robot? An exploratory pilot study using participatory arts and social robotics to improve psychological well-being in later life. Aging Ment Health 2021; 25: 575–84. doi:10.1080/13607863.2019.1699016
110 Whelan S, Burke M, Barrett E, et al. The effects of MARIO, A social robot, on the resilience of people with dementia: a multiple case study. Gerontechnology 2020; 20: 1–16. doi:10.4017/gt.2020.20.1.413.10
111 Pu L, Moyle W, Jones C. How people with dementia perceive a therapeutic robot called PARO in relation to their pain and mood: a qualitative study. J Clin Nurs 2020; 29: 437–46. doi:10.1111/jocn.15104
112 Lu L-C, Lan S-H, Hsieh Y-P, et al. Effectiveness of companion robot care for dementia: a systematic review and meta-analysis. Innov Aging 2021; 5: 2. doi:10.1093/geroni/igab013
113 Bernabei V, De Ronchi D, La Ferla T, et al. Animal-assisted interventions for elderly patients affected by dementia or psychiatric disorders: a review. J Psychiatr Res 2013; 47: 762–73. doi:10.1016/j.jpsychires.2012.12.014
114 Guisado-Fernandez E et al. A smart health platform for measuring health and well-being improvement in people with dementia and their informal caregivers: usability study. JMIR Res Protoc 2019; 8: 8. doi:10.2196/13280
115 Saragih ID, Tonapa SI, Sun TL, et al. Effects of robotic care interventions for dementia care: a systematic review and meta-analysis randomised controlled trials. J Clin Nurs 2021; 30: 3139–52. doi:10.1111/jocn.15856
116 Hirt J, Ballhausen N, Hering A, et al. Social robot interventions for people with dementia: a systematic review on effects and quality of reporting. J Alzheimers Dis 2021; 79: 773–92. doi:10.3233/JAD-200347
117 Chen K, Lou V-Q, Tan K-K, et al. Effects of a humanoid companion robot on dementia symptoms and caregiver distress for residents in long-term care. J Am Med Dir Assoc 2020; 21: 1724–8. doi:10.1016/j.jamda.2020.05.036
118 Dinesen B, Hansen HK, Grønborg GB, et al. Use of a social robot (LOVOT) for persons with dementia: exploratory study. JMIR Rehabil Assist Technol 2022; 9: e36505. doi:10.2196/36505
119 Boatman F, Golden M, Jin J, et al. Assistive technology: visual mapping combined with mobile software can enhance quality of life and ability to carry out activities of daily living in individuals with impaired memory. Technol Health Care 2020; 28: 121–8. doi:10.3233/THC-191980
120 Liang A, Piroth I, Robinson H, et al. A pilot randomized trial of a companion robot for people with dementia living in the community. J Am Med Dir Assoc 2017; 18: 871–8. doi:10.1016/j.jamda.2017.05.019
121 Dada S, van der Walt C, May AA, et al. Intelligent assistive technology devices for persons with dementia: a scoping review. Assistive Technology 2021: 1–14. doi:10.1080/10400435.2021.1992540
122 Leng M, Liu P, Zhang P, et al. Pet robot intervention for people with dementia: a systematic review and meta-analysis of randomized controlled trials. Psychiatry Res 2019; 271: 516–25. doi:10.1016/j.psychres.2018.12.032
123 Mizuno J, Sadohara K, Nihei M, et al. The application of an information support robot to reduce agitation in an older adult with Alzheimer’s disease living alone in a community dwelling: a case study. Hong Kong J Occup Ther 2021; 34: 50–9. doi:10.1177/15691861211005059
124 van Santen J, Dröes R-M, Twisk JWR, et al. Effects of exergaming on cognitive and social functioning of people with dementia: a randomized controlled trial. J Am Med Dir Assoc 2020; 21: 1958–67. doi:10.1016/j.jamda.2020.04.018
125 Finkel SI, Costa e Silva J, Cohen G, et al. Behavioral and psychological signs and symptoms of dementia: a consensus statement on current knowledge and implications for research and treatment. Int Psychogeriatr 1996; 8 Suppl 3: 497–500. doi:10.1017/s1041610297003943
126 Park S, Bak A, Kim S, et al. Animal-assisted and pet-robot interventions for Ameliorating behavioral and psychological symptoms of dementia: a systematic review and meta-analysis. Biomedicines 2020; 8: 6. doi:10.3390/biomedicines8060150
127 Ong YC, Tang A, Tam W. Effectiveness of robot therapy in the management of behavioural and psychological symptoms for individuals with dementia: a systematic review and meta-analysis. J Psychiatr Res 2021; 140: 381–94. doi:10.1016/j.jpsychires.2021.05.077
128 Jøranson N, Olsen C, Calogiuri G, et al. Effects on sleep from group activity with a robotic seal for nursing home residents with dementia: a cluster randomized controlled trial. Int Psychogeriatr 2021; 33: 1045–56. doi:10.1017/S1041610220001787
129 Jøranson N, Pedersen I, Rokstad AMM, et al. Effects on symptoms of agitation and depression in persons with dementia participating in robot-assisted activity: a cluster-randomized controlled trial. J Am Med Dir Assoc 2015; 16: 867–73. doi:10.1016/j.jamda.2015.05.002
130 Moyle W, Cooke M, Beattie E, et al. Exploring the effect of companion robots on emotional expression in older adults with dementia: a pilot randomized controlled trial. J Gerontol Nurs 2013; 39: 46–53. doi:10.3928/00989134-20130313-03
131 Valentí Soler M, Agüera-Ortiz L, Olazarán Rodríguez J, et al. Social robots in advanced dementia. Front Aging Neurosci 2015; 7: 133. doi:10.3389/fnagi.2015.00133
132 Pu L, Moyle W, Jones C, et al. The effect of a social robot intervention on sleep and motor activity of people living with dementia and chronic pain: a pilot randomized controlled trial. Maturitas 2021; 144: 16–22. doi:10.1016/j.maturitas.2020.09.003
133 Elfrink TR, Ullrich C, Kunz M, et al. The online life story book: a randomized controlled trial on the effects of a digital reminiscence intervention for people with (very) mild dementia and their informal caregivers. PLoS One 2021; 16: e0256251. doi:10.1371/journal.pone.0256251
134 Wang X, Shen J, Chen Q. How PARO can help older people in elderly care facilities: a systematic review of RCT. Int J Nurs Knowl 2022; 33: 29–39. doi:10.1111/2047-3095.12327
135 Yamazaki R, Kase H, Nishio S, et al. Anxiety reduction through close communication with robotic media in dementia patients and healthy older adults. JRM 2020; 32: 32–42. doi:10.20965/jrm.2020.p0032
136 Chen L-Y, Sumioka H, Ke LJ, et al. Effects of teleoperated humanoid robot application in older adults with neurocognitive disorders in Taiwan: a report of three cases. Aging Med Healthc 2020; 11: 67–71. doi:10.33879/AMH.2020.066-2001.003
137 Olsson A, Skovdahl K, Engström M. Using diffusion of innovation theory to describe perceptions of a passive positioning alarm among persons with mild dementia: a repeated interview study. BMC Geriatr 2016; 16: 3. doi:10.1186/s12877-016-0183-8
138 Brims L, Oliver K. Effectiveness of assistive technology in improving the safety of people with dementia: a systematic review and meta-analysis. Aging Ment Health 2019; 23: 942–51. doi:10.1080/13607863.2018.1455805
139 Gilbert S, Harvey H, Melvin T, et al. Large language model AI chatbots require approval as medical devices. Nat Med 2023; 29: 2396–8. doi:10.1038/s41591-023-02412-6
140 Thirunavukarasu AJ, Ting DSJ, Elangovan K, et al. Large language models in medicine. Nat Med 2023; 29: 1930–40. doi:10.1038/s41591-023-02448-8
141 Wade B, Abraham J, Coder M. Health advances, LLC classification of digital health technologies; 2023.
142 Van der Roest HG, Wenborn J, Pastink C, et al. Assistive technology for memory support in dementia. Cochrane Database Syst Rev 2017; 6: CD009627. doi:10.1002/14651858.CD009627.pub2
143 Laird EA, Ryan A, McCauley C, et al. Using mobile technology to provide personalized reminiscence for people living with dementia and their carers: appraisal of outcomes from a quasi-experimental study. JMIR Ment Health 2018; 5: e57. doi:10.2196/mental.9684
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; Rasita Vinay 4
1 Department of Management, Technology and Economics, ETH Zürich, Zurich, Switzerland
2 University of St. Gallen, St. Gallen, Switzerland
3 Department of Management, Technology and Economics, ETH Zürich, Zurich, Switzerland; Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland; Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
4 Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland