Published on in Vol 7 (2024)

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Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review


1School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland

2Les Maisons de la Providence Nursing Home, Le Châble, Switzerland

3Faculty of Biology and Medicine, Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland

4Service of Old Age Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland

Corresponding Author:

Sofia Fernandes, MScN

School of Health Sciences

University of Applied Sciences and Arts Western Switzerland (HES-SO)

Chemin de l’Agasse 5

Sion, 1950


Phone: 41 00415860861


Background: The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence–based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early.

Objective: The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early.

Methods: A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach.

Results: A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD.

Conclusions: Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.

JMIR Nursing 2024;7:e54496



Demographic aging is a worldwide phenomenon, with significant growth in the number of older adults expected in the coming decades [1]. The number of people aged 80 years or older is expected to reach 426 million by 2050, with a high prevalence of dementia and other mental health disorders [2]. According to the World Health Organization, more than 55 million people worldwide endure dementia, and around 10 million new cases are diagnosed yearly [3]. More than 90% of them are affected by 1 or more of the behavioral and psychological symptoms of dementia (BPSD) and 80%-90% live in nursing homes. The BPSD, also known as neuropsychiatric symptoms related to dementia, is characterized by changes in behavior, perception, thought content, and mood [4]. The most common symptoms are apathy, aberrant motor behaviors, mood disturbances, aggression, anxiety, irritability, and sleep disorders [4]. The BPSD has a negative impact on the quality of life, accelerating functional decline and leading to earlier mortality [5,6]. The BPSD can be the source of social isolation, abuse, and burdens for informal caregivers [7,8]. For health care professionals, including nurses, managing BPSD can lead to work overload, stress, burnout, reduced quality of care, and the risks of patient abuse [9-11]. Finally, BPSD can increase health care system costs through more consultations, hospitalizations, and the prescription of more psychotropic drugs and mood stabilizers [12].

The etiopathogenesis of the BPSD is complex. Although dementia is a prerequisite for the onset of its behavioral and psychological symptoms, it is not the sole determinant. The BPSD can result from a convergence of factors, including neurological alterations, somatic problems, psychological factors, environmental conditions, and individual patient characteristics [13]. Moreover, the frequency, intensity, and types of symptoms vary considerably from 1 person to another. Thus, effectively managing the BPSD requires a structured approach that identifies and acts on various trigger factors [4,13-15]. The literature suggests different models or approaches, but all agree on three distinct steps which are (1) assessing manifestations of the BPSD that the patients present with, (2) formulating a hypothesis to help understand them, and (3) designing 1 or more interventions targeting their trigger factors [4,13-15]. Interventions can be psychosocial, pharmacological, or a combination of both [4,13-15]. Traditionally, detecting symptoms, monitoring their evolution, and evaluating treatment efficacy are based on nursing observations documented using assessment scales (Neuropsychiatric Inventory and Cohen-Mansfield Agitation Inventory) or solely using written notes in the patient’s medical record [15,16]. However, this process may prove ineffective for the early detection of the signs of BPSD, and nurses may perceive it as a potential factor in work overload [12]. Indeed, the first 2 steps require the investment of health care professionals, informal caregivers, and other individuals, and the third is more complex due to the variability and multifactorial nature of the BPSD. The BPSD challenges nurses daily, often triggering crises that are extremely complex to manage. Responding effectively and efficiently to these clinical issues requires more intensive observation and specialized care, with a greater emphasis on the prodromal detection of warning signs. However, due to an aging workforce and difficult working conditions (eg, high levels of stress and burnout, job dissatisfaction, and low levels of retention), the health care sector is facing a shortage of nursing staff [17]. The International Council of Nurses estimates a need for 13 million extra nurses to fill the worldwide shortages in the profession [18]. This shows the limitations of current human resources–based strategies, with the corollary need to explore innovative and sustainable solutions.

In recent decades, new information technologies have been adopted by every area of health care [19]. The first information technologies to be integrated into health care were electronic medical and health records, clinical information systems, and health information exchanges. More recently, other technologies have emerged, such as clinical decision support systems, mobile health apps, telehealth, telemedicine, robotics, wireless medical devices, and virtual reality [19-21]. Technological development in the health care sector, including nursing, is currently focused on artificial intelligence (AI) [19]. In the field of health care, AI usually refers to software capable of interpreting clinical data, learning from it, and helping clinical decision-making [19,22]. Combined with critical thinking and human judgment, AI has the potential to improve nurses’ clinical reasoning by increasing the speed and accuracy of assessment, anticipation, synthesis, and knowledge generation [23]. From 1985 to date, the PubMed database lists 1086 publications on AI in nursing. There has been a significant growth in the number of these publications since 2020, reinforcing the nursing sciences’ aims of developing and adapting nursing practices in line with sociodemographic changes and health care system, and medical and technical progress. [24]. Promoting the development, adoption and effective use of AI-based technologies (AITs) in health care has been identified as a key strategy to address the challenges related to both the complexity of managing the BPSD and limited resources [19,25-28]. Despite significant recent interest in the topic and the increasing number of technical devices on the market, little information is available on introducing AITs to help nurses attempting to detect BPSD as early as possible. This narrative review aims to identify and summarize the characteristics of existing publications concerning the use of AITs to support nurses in the early identification and management of BPSD.

Search Strategy

This narrative review was conducted following the Toronto and Remington guidelines [29]. The research question used to guide it was as follows:

What are the available publications on the use of artificial-intelligence-based technologies in neuropsychiatric symptoms related to dementia?

We consulted the PubMed database in September 2023 using the descriptors and keywords “artificial intelligence,” “behavioural and psychological symptoms of dementia,” and “neuropsychiatric symptoms” (Multimedia Appendix 1).

Eligibility Criteria

Publications addressing the concept of dementia were included because the literature often links the concepts of dementia and the BPSD. The inclusion and exclusion criteria are presented in Textbox 1.

A total of 30 publications were identified and included after their titles and abstracts were reviewed. Following a thorough examination of their full texts, 5 publications were excluded because they focused on mental health issues unrelated to dementia or BPSD. In total, 25 publications were included.

Textbox 1. Inclusion and exclusion criteria.

Inclusion criteria

  • Mental health disorder
    • Dementia
    • Behavioral and psychological symptoms of dementia
  • Health care setting
    • No restrictions
  • Artificial intelligence–based technology type
    • No restrictions
  • Artificial intelligence–based technology use
    • No restrictions
  • Publication type
    • No restrictions
  • Publication date
    • No restrictions
  • Language
    • No restrictions

Exclusion criteria

  • Mental health disorder
    • Other mental health disorders

Data Extraction and Synthesis

Information extracted from the publications retained for analysis included study design, country, journal title and category, mental health disorder addressed, type, subtype, use of the AITs, and health care setting. The type, subtype, and use of each AIT were identified via a basic qualitative content analysis, based on the authors’ stated aims and objectives (information found in the introduction and methods sections of the papers retained). In the context of mental health care, the type of AIT used was categorized according to the groups proposed by Jin et al [30], which are, machine learning, natural language processing, and digital health. Once this information was extracted, keywords were chosen to categorize the AIT’s use, with keywords determined based on the verbs used in each paper’s objectives (eg, measure, evaluate, screen, manage, and predict). The results are reported using a narrative approach.

Study Characteristics

Publications from 14 countries were identified, with publication dates ranging from 2006 to 2023 (Figure 1). A total of 8 publications addressed acute care settings [31-38], 6 looked at nursing homes [39-44], and 3 examined community care [45-47]. The majority described prospective observational studies published in journals covering geriatrics and psychogeriatrics [31,33,34,36,46-48] (Multimedia Appendix 2) [31-55].

Figure 1. Number of publications per country.

Uses of AITs

A total of 12 publications reported using machine learning–type AITs, including the facial expression recognition and predictive modeling subtypes [31,34-38,43,46,47,49,50,55], 11 publications explored digital health–type AITs, including the wearable technologies and robotic subtypes [32,39-42,44,45, 51-54], and 2 publications examined natural language processing–type AITs [33,48] (Figure 2).

We identified three categories of publications depending on the AIT’s use and they are (1) predicting behavior and the stage and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and the BPSD (Figure 3).

A total of 4 publications reported on the use of machine learning technology to predict dementia behavior and the stage and progression of dementia [34,36-38]. Three publications referred to the use of natural language processing for screening and assessing clinical symptoms [35,48,49] and 1 reported on the use of machine learning [50]. One publication described the use of wearable technologies [40], and 1 combined this type of AIT with machine learning [46]. Finally, 10 publications reported on the use of robotics as a psychosocial approach to managing dementia and BPSD [32,39,41,42,44,45,51-54] (Table 1).

One publication reported on the use of AITs to predict the stage and progression of dementia [31], 1 described the detection and measurement of dementia’s clinical symptoms [48], and 6 examined dementia management [39,41,44,51-53]. As for the BPSD, 5 publications reported using AITs to predict behavior [33,36,37,46,47], 3 described the detection and assessment of clinical symptoms [35,40,49], and 5 looked at managing behavior [32,42-44,55] (Figure 4). In the context of the BPSD, behavior management refers to interventions carried out to identify and act on trigger factors.

Figure 2. Number of publications by type of artificial intelligence–based technology.
Figure 3. Number of publications by artificial intelligence–based technology use.
Table 1. Publications by type of AITa and use.
ReferenceAIT typesAITs subtypeAITs use

Machine learningNatural language processingDigital health
PredictScreen and measureManage
Al-Harrasi et al [48]


Byeon [31]

Predictive modeling

Chen et al [39]


Chen et al [47]

Facial expression recognition

Cho et al [46]

Predictive modeling

Demange et al [32]


Eikelboom et al [33]


Favela et al [40]

Wearable technology

Filan and Llewellyn-Jones [51]


Gill et al [34]


Hsieh et al [41]


Jøranson et al [44]


König et al [49]


König et al [35]


Leng et al [52]


Liang et al [45]


Mallo et al [36]


Mar et al [38]


Mar et al [37]


Moyle et al [42]


Pu et al [53]


Russo et al [55]


Shah et al [50]


Tadokoro et al [43]

Facial expression recognition

Yu et al [54]


aAIT: artificial intelligence–based technology.

bN/A: not applicable.

Figure 4. Number of publications by artificial intelligence–based technology use and mental health issues. BPSD: behavioral and psychological symptoms of dementia.

Principal Results

Despite the growing interest in AITs, most of those currently used take a psychosocial approach to treating and caring for patients with BPSD by using the clinical signs that are already present. AI remains largely unexplored in terms of its potential for the early, real-time detection of BPSD. Yet, in different health care settings and contexts, AITs could provide nurses with accurate and reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions [27,56]. Based on our findings, it appears that the use of AITs has been explored more in acute care than in long-term care settings, which include community care and nursing homes. However, the prevalence of BPSD seems to be higher in the context of long-term care, particularly in nursing homes where 80%-90% of residents exhibit at least 1 of the BPSD and institutional resources tend to be more limited [57].

As mentioned above, BPSD has traditionally been assessed and monitored by health care professionals’ observations of patients’ behaviors [15,16]. However, this process may have limited success in the early detection of warning signs of the BPSD, and health care professionals perceive it to be another task or factor leading to work overload [11]. Therefore, it seems appropriate to anticipate symptom escalation and optimize staff and financial resources. Multimodal sensors for capturing physiological parameters, activity trackers, and facial expression recognition are all promising AITs that make the process of managing the BPSD more efficient and personalized [40,58-61]. By mining information from such devices, nurses could detect early warning signs of BPSD and their trigger factors. By combining this information with their clinical knowledge and experience, nurses could be equipped with a clinical decision-making support system enabling them to guide and personalize their therapeutic interventions [56,62-64].

Although nurses agree about the potential usefulness of AITs, most do not fully understand AI’s underlying principles, and they are concerned about the potential consequences of its use in clinical practice [65-68]. Other obstacles pertaining to AITs include the unknown cost-benefits of their use in health care settings, the current lack of use and data management protocols in those settings, and the lack of information technology capacity there to support them [69,70]. The published papers identified in this review reinforced these points as the involvement of nurses in designing studies and the use of AITs was low. However, in interdisciplinary contexts, nurses have key roles to play in the conception and design of AIT devices, verifying their effectiveness and adapting their use.

Strengths and Limitations

The characteristics of the publications retained in this narrative review revealed the countries and contexts where AITs have been integrated into settings dealing with the BPSD and have been investigated. It also demonstrated the types of technologies available and their intended purposes, as well as the clinical contexts in which they are deployed. These results, while not exhaustive, provide a preliminary overview of this emerging topic and identify AITs’ potential benefits for clinical practice and pathways for future research. This narrative review has some limitations, nevertheless. The absence of an assessment of the quality and validity of the selected publications may bias the quality of their reported outcomes. Furthermore, including publications that address dementia could lead to confusion regarding this narrative review’s focus. Although the concepts of dementia and BPSD are frequently interrelated in the specialized literature, including the concept of dementia could lead readers to misunderstand the scope of the results presented.


AI has the potential to transform nursing practice, particularly in support of the diagnosis and management of BPSD, which are currently among the major challenges in caring for older adults with dementia. However, our literature review found little experimental evidence, data, or understanding of how these types of technologies could be applied advantageously to the early detection of BPSD by nurses. Furthermore, although these are preliminary findings, the results of this review showed that research on this topic has only been done in relatively few countries, despite the impact of the BPSD being a global phenomenon. Based on this fact and the review’s limitations, we would recommend that a more comprehensive examination be performed, such as a scoping review, to meticulously explore the research conducted on AITs for the early detection of BPSD. It also seems important that future experimental research investigates the effectiveness, feasibility, and acceptability of using devices based on AITs for the prodromal detection of BPSD. Specific research in long-term care settings seems to be particularly lacking. Nurses are intimately involved in creating a vision of contemporary professional nursing practice and then applying that practice. Therefore, it seems appropriate that they should be involved in strengthening collaboration with information technology engineers and programmers. Nurses’ perceptions and experiences of using AITs to detect BPSD should also be explored, using a qualitative approach, as should how the data provided by these types of technologies contribute to nurses’ clinical reasoning and decision-making processes.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Search query.

DOCX File , 31 KB

Multimedia Appendix 2

Description of the selected publications.

DOCX File , 25 KB

  1. Vieillissement et santé. World Health Organization. 2022. URL: [accessed 2023-09-06]
  2. von Gunten A. Psychiatrie de la personne âgée en Suisse. Swiss Arch Neurol Psychiatr Psychother. 2017;168(02):33-40. [FREE Full text] [CrossRef]
  3. Démence. Organisation mondiale de la santé [World Health Organization]. 2023. URL: [accessed 2023-09-06]
  4. Kales HC, Lyketsos CG, Miller EM, Ballard C. Management of behavioral and psychological symptoms in people with Alzheimer's disease: an international Delphi consensus. Int Psychogeriatr. 2019;31(1):83-90. [CrossRef] [Medline]
  5. Bränsvik V, Granvik E, Minthon L, Nordström P, Nägga K. Mortality in patients with behavioural and psychological symptoms of dementia: a registry-based study. Aging Ment Health. 2021;25(6):1101-1109. [FREE Full text] [CrossRef] [Medline]
  6. Cepoiu-Martin M, Tam-Tham H, Patten S, Maxwell CJ, Hogan DB. Predictors of long-term care placement in persons with dementia: a systematic review and meta-analysis. Int J Geriatr Psychiatry. 2016;31(11):1151-1171. [CrossRef] [Medline]
  7. Feast A, Orrell M, Charlesworth G, Melunsky N, Poland F, Moniz-Cook E. Behavioural and psychological symptoms in dementia and the challenges for family carers: systematic review. Br J Psychiatry. 2016;208(5):429-434. [FREE Full text] [CrossRef] [Medline]
  8. Pinyopornpanish K, Soontornpun A, Wongpakaran T, Wongpakaran N, Tanprawate S, Pinyopornpanish K, et al. Impact of behavioral and psychological symptoms of Alzheimer's disease on caregiver outcomes. Sci Rep. 2022;12(1):14138. [FREE Full text] [CrossRef] [Medline]
  9. Kang Y, Hur Y. Nurses' experience of nursing workload-related issues during caring patients with dementia: a qualitative meta-synthesis. Int J Environ Res Public Health. 2021;18(19):10448. [FREE Full text] [CrossRef] [Medline]
  10. Ostaszkiewicz J, Lakhan P, O'Connell B, Hawkins M. Ongoing challenges responding to behavioural and psychological symptoms of dementia. Int Nurs Rev. 2015;62(4):506-516. [CrossRef] [Medline]
  11. Steele C, Berry K, Brown LJE. Healthcare professionals' experiences of using a biopsychosocial approach to understand behavioural and psychological symptoms of dementia: a qualitative interview study. Int J Older People Nurs. 2022;17(2):e12427. [CrossRef] [Medline]
  12. Butler M, Gaugler JE, Talley KMC, Abdi HI, Desai PJ, Duval S, et al. Care interventions for people living with dementia and their caregivers. Comparative Effectiveness Review No. 231. Agency for Healthcare Research and Quality. Rockville, MD. URL: https:/​/effectivehealthcare.​​sites/​default/​files/​pdf/​cer-231-dementia-interventions-final_0.​pdf [accessed 2024-05-08]
  13. Dementia: Assessment, Management and Support for People Living with Dementia and Their Carers. London. National Institute for Health and Care Excellence; 2018.
  14. Ma H, Lu X, Zhou A, Wang F, Zuo X, Zhan M, et al. Clinical practice guidelines for the management of behavioral and psychological symptoms of dementia: a systematic review with AGREE II. Front Neurol. 2022;13:799723. [FREE Full text] [CrossRef] [Medline]
  15. Tible OP, Riese F, Savaskan E, von Gunten A. Best practice in the management of behavioural and psychological symptoms of dementia. Ther Adv Neurol Disord. 2017;10(8):297-309. [FREE Full text] [CrossRef] [Medline]
  16. Bessey LJ, Walaszek A. Management of behavioral and psychological symptoms of dementia. Curr Psychiatry Rep. 2019;21(8):66. [CrossRef] [Medline]
  17. Drennan VM, Ross F. Global nurse shortages-the facts, the impact and action for change. Br Med Bull. 2019;130(1):25-37. [FREE Full text] [CrossRef] [Medline]
  18. ICN policy brief—the global nursing shortage and nurse retention. International Council of Nurses. 2021. URL: [accessed 2023-09-06]
  19. Raymond L, Castonguay A, Doyon O, Paré G. Nurse practitioners' involvement and experience with AI-based health technologies: a systematic review. Appl Nurs Res. 2022;66:151604. [CrossRef] [Medline]
  20. Golinelli D, Boetto E, Carullo G, Nuzzolese AG, Landini MP, Fantini MP. Adoption of digital technologies in health care during the COVID-19 pandemic: systematic review of early scientific literature. J Med Internet Res. 2020;22(11):e22280. [FREE Full text] [CrossRef] [Medline]
  21. Lu L, Zhang J, Xie Y, Gao F, Xu S, Wu X, et al. Wearable health devices in health care: narrative systematic review. JMIR Mhealth Uhealth. 2020;8(11):e18907. [FREE Full text] [CrossRef] [Medline]
  22. Douthit BJ, Hu X, Richesson RL, Kim H, Cary MP. How artificial intelligence is transforming the future of nursing. Am J Nurs. 2020;15(9):100-102. [FREE Full text]
  23. Artificial intelligence, critical thinking, and the nursing process. HIMSS. 2019. URL: [accessed 2023-09-06]
  24. Pepin J, Ducharme F, Kérouac S. La Pensée Infirmière, 4e édition. Montréal. Chenelière éducation; 2017.
  25. Boyne JJ, Ski CF, Fitzsimons D, Amin H, Hill L, Thompson DR. The changing role of patients, and nursing and medical professionals as a result of digitalization of health and heart failure care. J Nurs Manag. 2022;30(8):3847-3852. [FREE Full text] [CrossRef] [Medline]
  26. The Topol review: preparing the healthcare workforce to deliver the digital future. NHS Health Education England. 2019. URL: [accessed 2023-09-06]
  27. Stavropoulos TG, Papastergiou A, Mpaltadoros L, Nikolopoulos S, Kompatsiaris I. IoT wearable sensors and devices in elderly care: a literature review. Sensors (Basel). 2020;20(10):2826. [FREE Full text] [CrossRef] [Medline]
  28. Global Strategy on Digital Health 2020-2025. Geneva. World Health Organization; 2021.
  29. Toronto CE, Remington R. A Step-by-Step Guide to Conducting an Integrative Review. Cham. Springer International Publishing; 2020.
  30. Jin KW, Li Q, Xie Y, Xiao G. Artificial intelligence in mental healthcare: an overview and future perspectives. Br J Radiol. 2023;96(1150):20230213. [FREE Full text] [CrossRef] [Medline]
  31. Byeon H. Predicting the severity of Parkinson's disease dementia by assessing the neuropsychiatric symptoms with an SVM regression model. Int J Environ Res Public Health. 2021;18(5):2551. [FREE Full text] [CrossRef] [Medline]
  32. Demange M, Lenoir H, Pino M, Cantegreil-Kallen I, Rigaud AS, Cristancho-Lacroix V. Improving well-being in patients with major neurodegenerative disorders: differential efficacy of brief social robot-based intervention for 3 neuropsychiatric profiles. Clin Interv Aging. 2018;13:1303-1311. [FREE Full text] [CrossRef] [Medline]
  33. Eikelboom WS, Singleton EH, van den Berg E, de Boer C, Coesmans M, Goudzwaard JA, et al. The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study. Alzheimers Res Ther. 2023;15(1):94. [FREE Full text] [CrossRef] [Medline]
  34. Gill S, Mouches P, Hu S, Rajashekar D, MacMaster FP, Smith EE, et al. Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data. J Alzheimers Dis. 2020;75(1):277-288. [FREE Full text] [CrossRef]
  35. König A, Mallick E, Tröger J, Linz N, Zeghari R, Manera V, et al. Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis. Eur Psychiatry. 2021;64(1):e64. [FREE Full text] [CrossRef] [Medline]
  36. Mallo SC, Valladares-Rodriguez S, Facal D, Lojo-Seoane C, Fernández-Iglesias MJ, Pereiro AX. Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach. Int. Psychogeriatr. 2019;32(3):381-392. [CrossRef]
  37. Mar J, Gorostiza A, Ibarrondo O, Cernuda C, Arrospide A, Iruin Á, et al. Validation of random forest machine learning models to predict dementia-related neuropsychiatric symptoms in real-world data. J Alzheimers Dis. 2020;77(2):855-864. [FREE Full text] [CrossRef] [Medline]
  38. Mar J, Gorostiza A, Arrospide A, Larrañaga I, Alberdi A, Cernuda C, et al. Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data. Rev Psiquiatr Salud Ment (Engl Ed). Mar 25, 2021;15(3):167-175. [CrossRef] [Medline]
  39. Chen K, Lou VWQ, Tan KCK, Wai MY, Chan LL. 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(11):1724-1728.e3. [CrossRef] [Medline]
  40. Favela J, Cruz-Sandoval D, Morales-Tellez A, Lopez-Nava IH. Monitoring behavioral symptoms of dementia using activity trackers. J Biomed Inform. 2020;109:103520. [FREE Full text] [CrossRef] [Medline]
  41. Hsieh CJ, Li PS, Wang CH, Lin SL, Hsu TC, Tsai CMT. 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(8):1027-1042. [FREE Full text] [CrossRef] [Medline]
  42. Moyle W, Bramble M, Jones CJ, Murfield JE. "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(1):177-185. [FREE Full text] [CrossRef] [Medline]
  43. Tadokoro K, Yamashita T, Kawano S, Sato J, Omote Y, Takemoto M, et al. Immediate beneficial effect of makeup therapy on behavioral and psychological symptoms of dementia and facial appearance analyzed by artificial intelligence software. J Alzheimers Dis. 2021;83(1):57-63. [CrossRef] [Medline]
  44. Jøranson N, Pedersen I, Rokstad AMM, Ihlebæk C. 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(10):867-873. [CrossRef] [Medline]
  45. Liang A, Piroth I, Robinson H, MacDonald B, Fisher M, Nater UM, 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(10):871-878. [CrossRef] [Medline]
  46. Cho E, Kim S, Heo SJ, Shin J, Hwang S, Kwon E, et al. Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation. Sci Rep. 2023;13(1):8073. [FREE Full text] [CrossRef] [Medline]
  47. Chen LY, Tsai TH, Ho A, Li CH, Ke LJ, Peng LN, et al. Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system. Aging (Albany NY). 2022;14(3):1280-1291. [FREE Full text] [CrossRef] [Medline]
  48. Al-Harrasi AM, Iqbal E, Tsamakis K, Lasek J, Gadelrab R, Soysal P, et al. Motor signs in Alzheimer's disease and vascular dementia: detection through natural language processing, co-morbid features and relationship to adverse outcomes. Exp Gerontol. 2021;146:111223. [CrossRef] [Medline]
  49. König A, Linz N, Zeghari R, Klinge X, Tröger J, Alexandersson J, et al. Detecting apathy in older adults with cognitive disorders using automatic speech analysis. J Alzheimers Dis. 2019;69(4):1183-1193. [CrossRef] [Medline]
  50. Shah J, Rahman Siddiquee MM, Krell-Roesch J, Syrjanen JA, Kremers WK, Vassilaki M, et al. Neuropsychiatric symptoms and commonly used biomarkers of Alzheimer's disease: a literature review from a machine learning perspective. J Alzheimers Dis. 2023;92(4):1131-1146. [CrossRef] [Medline]
  51. Filan SL, Llewellyn-Jones RH. Animal-assisted therapy for dementia: a review of the literature. Int Psychogeriatr. 2006;18(4):597-611. [CrossRef] [Medline]
  52. Leng M, Liu P, Zhang P, Hu M, Zhou H, Li G, et al. Pet robot intervention for people with dementia: a systematic review and meta-analysis of randomized controlled trials. Psychiatry Res. 2019;271:516-525. [CrossRef] [Medline]
  53. Pu L, Coppieters MW, Byrnes J, Jones C, Smalbrugge M, Todorovic M, et al. Feasibility study protocol of the PainChek app to assess the efficacy of a social robot intervention for people with dementia. J Adv Nurs. 2022;78(2):587-594. [CrossRef] [Medline]
  54. Yu C, Sommerlad A, Sakure L, Livingston G. 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. [FREE Full text] [CrossRef] [Medline]
  55. Russo FA, Mallik A, Thomson Z, de Raadt St James A, Dupuis K, Cohen D. Developing a music-based digital therapeutic to help manage the neuropsychiatric symptoms of dementia. Front Digit Health. 2023;5:1064115. [FREE Full text] [CrossRef] [Medline]
  56. Pappadà A, Chattat R, Chirico I, Valente M, Ottoboni G. Assistive technologies in dementia care: an updated analysis of the literature. Front Psychol. 2021;12:644587. [FREE Full text] [CrossRef] [Medline]
  57. Cloak N, Al Khalili Y. Behavioral and psychological symptoms in dementia. In: StatPearls. Treasure Island, FL. StatPearls Publishing; 2022.
  58. Goerss D, Hein A, Bader S, Halek M, Kernebeck S, Kutschke A, et al. Automated sensor-based detection of challenging behaviors in advanced stages of dementia in nursing homes. Alzheimers Dement. 2020;16(4):672-680. [FREE Full text] [CrossRef] [Medline]
  59. Iaboni A, Spasojevic S, Newman K, Schindel Martin L, Wang A, Ye B, et al. Wearable multimodal sensors for the detection of behavioral and psychological symptoms of dementia using personalized machine learning models. Alzheimers Dement (Amst). 2022;14(1):e12305. [FREE Full text] [CrossRef] [Medline]
  60. Kyriakou K, Resch B, Sagl G, Petutschnig A, Werner C, Niederseer D, et al. Detecting moments of stress from measurements of wearable physiological sensors. Sensors (Basel). 2019;19(17):3805. [FREE Full text] [CrossRef] [Medline]
  61. Melander CA, Kikhia B, Olsson M, Wälivaara BM, Sävenstedt S. The impact of using measurements of electrodermal activity in the assessment of problematic behaviour in dementia. Dement Geriatr Cogn Dis Extra. 2018;8(3):333-347. [FREE Full text] [CrossRef] [Medline]
  62. Dada S, van der Walt C, May AA, Murray J. Intelligent assistive technology devices for persons with dementia: a scoping review. Assist Technol. 2022:1-14. [CrossRef] [Medline]
  63. Husebo BS, Heintz HL, Berge LI, Owoyemi P, Rahman AT, Vahia IV. Sensing technology to monitor behavioral and psychological symptoms and to assess treatment response in people with dementia. a systematic review. Front Pharmacol. 2019;10:1699. [FREE Full text] [CrossRef] [Medline]
  64. Lai Kwan C, Mahdid Y, Motta Ochoa R, Lee K, Park M, Blain-Moraes S. Wearable technology for detecting significant moments in individuals with dementia. Biomed Res Int. 2019;2019:6515813. [FREE Full text] [CrossRef] [Medline]
  65. Abuzaid MM, Elshami W, Fadden SM. Integration of artificial intelligence into nursing practice. Health Technol (Berl). 2022;12(6):1109-1115. [FREE Full text] [CrossRef] [Medline]
  66. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted influences of artificial intelligence on nursing education: scoping review. JMIR Nurs. 2021;4(1):e23933. [FREE Full text] [CrossRef] [Medline]
  67. Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Front Artif Intell. 2020;3:578983. [FREE Full text] [CrossRef] [Medline]
  68. Topaz M, Pruinelli L. Big data and nursing: implications for the future. Stud Health Technol Inform. 2017;232:165-171. [Medline]
  69. Olaye IM, Seixas AA. The gap between AI and bedside: participatory workshop on the barriers to the integration, translation, and adoption of digital health care and AI startup technology into clinical practice. J Med Internet Res. 2023;25:e32962. [FREE Full text] [CrossRef] [Medline]
  70. Tachkov K, Zemplenyi A, Kamusheva M, Dimitrova M, Siirtola P, Pontén J, et al. Barriers to use artificial intelligence methodologies in health technology assessment in Central and East European countries. Front Public Health. 2022;10:921226. [FREE Full text] [CrossRef] [Medline]

AI: artificial intelligence
AIT: artificial intelligence–based technology
BPSD: behavioral and psychological symptoms of dementia

Edited by E Borycki, K Cato; submitted 12.11.23; peer-reviewed by A Hidki, L Nunes, A Haddadi Avval; comments to author 20.02.24; revised version received 15.04.24; accepted 26.04.24; published 28.05.24.


©Sofia Fernandes, Armin von Gunten, Henk Verloo. Originally published in JMIR Nursing (, 28.05.2024.

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