An Interdisciplinary Approach to Long-Term Care: Artificial Intelligence Applications for Monitoring Nutritional Status and Social Well-Being

Abstract

The global rise in the older adult population brings complex, interrelated biopsychosocial challenges, including nutritional inadequacies, sarcopenia, social isolation, and sleep disturbances. Dealing with these interconnected issues in institutional care settings is increasingly difficult given the restrictions of traditional approaches. This descriptive review analyzes, from a multidisciplinary perspective, the role of Artificial Intelligence (AI)-based technologies in monitoring nutritional status, predicting clinical risks such as malnutrition and sarcopenia, and enhancing psychosocial well-being in elder care. Relevant literature was identified through searches in PubMed/MEDLINE, Scopus, and Web of Science (2016–2026), focusing on peer-reviewed studies published in English. Current literature indicates that AI-based image-processing systems can accurately monitor dietary intake, while machine learning algorithms can enable earlier risk stratification for sarcopenia and inflammatory trajectories using biomarker data. Furthermore, social robots and non-contact sensors have been shown to reduce loneliness among older adults, improve sleep quality, and indirectly enhance motivation for eating. From a social work perspective, AI is considered an effective tool that increases organizational efficiency in case management and facilitates a shift from crisis-oriented intervention to predictive and preventive care models. The success of this technical transformation depends on establishing an ethical framework that supports privacy, dignity, and the principles of person-centered care. Overall, AI provides evidence-informed decision support for dietitians and social work professionals, helping to develop a holistic care ecosystem that optimizes the well-being of older adults.

Keywords

• Artificial intelligence
• elder care
• nutrition
• social isolation
• sarcopenia
• social work

Uzun Süreli Bakımda Disiplinlerarası Bir Yaklaşım: Beslenme Durumu ve Sosyal Refahın İzlenmesinde Yapay Zekâ Uygulamaları

Abstract

Yaşlı nüfusun küresel olarak artması, beslenme yetersizlikleri, sarkopeni, sosyal izolasyon ve uyku bozuklukları gibi karmaşık ve birbirleriyle ilişkili biyopsikososyal zorlukları beraberinde getirmektedir. Geleneksel yaklaşımların sınırlılıkları nedeniyle, kurumsal bakım ortamlarında bu birbiriyle bağlantılı sorunların yönetimi giderek daha güç hale gelmiştir. Bu anlatı derlemesi, yaşlı bakımında yapay zekâ (YZ) tabanlı teknolojilerin beslenme durumunun izlenmesindeki, malnütrisyon ve sarkopeni dâhil klinik risklerin öngörülmesindeki ve psikososyal refahın artırılmasındaki rolünü multidisipliner bir bakış açısıyla değerlendirmektedir. Güncel literatür, YZ destekli görüntü işleme sistemlerinin besin alımını yüksek doğrulukla izleyebildiğini; makine öğrenmesi algoritmalarının ise biyobelirteç verilerini kullanarak sarkopeni ve inflamatuvar seyre yönelik erken risk sınıflandırmasına olanak tanıdığını göstermektedir. Ayrıca sosyal robotların ve temassız sensörlerin yaşlılarda yalnızlığı azaltabildiği, uyku kalitesini iyileştirebildiği ve dolaylı olarak yeme motivasyonunu artırabildiği bildirilmektedir. Sosyal hizmet perspektifinden, YZ vaka yönetiminde kurumsal verimliliği artıran ve kriz odaklı müdahaleden öngörülü ve önleyici bakım modellerine geçişi destekleyen stratejik bir araç olarak görülmektedir. Bununla birlikte, bu teknolojik dönüşümün başarısı mahremiyeti, onuru ve kişi merkezli bakım ilkelerini koruyan bir etik çerçevenin oluşturulmasına bağlıdır. Genel olarak YZ, diyetisyenler ve sosyal hizmet uzmanları için kanıta dayalı karar desteği sunmakta ve yaşlı bireylerin iyilik hâlini optimize eden bütüncül bir bakım ekosisteminin geliştirilmesine katkı sağlamaktadır.

Keywords

• Yapay zeka
• yaşlı bakımı
• beslenme
• sosyal izolasyon
• sarkopeni
• sosyal hizmet

References

1. 1. Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in hospitals and clinics: transforming healthcare in the 21st Century. Bioengineering. 2024;11(4):337.
2. 2. Kurra RK, Molli SM. AI in nutrition, dental, and healthcare: transforming dietary science for comprehensive well-being. In: Modernizing the Food Industry: AI-Powered Infrastructure, Security, and Supply Chain Innovation. IGI Global Scientific Publishing; 2026:305-328.
3. 3. Janssen SMW, Bouzembrak Y, Tekinerdogan B. Artificial intelligence in malnutrition: a systematic literature review. Advances in Nutrition. 2024;15(9):100264.
4. 4. Bernstein AM, Janeke P, Riggs R V, et al. Artificial intelligence-based hospital malnutrition screening: validation of a novel machine learning model. Clinical Informatics. 2025;16(5):1646-1657. doi: 10.1055/a-2635-3158
5. 5. Støre SJ, Beckman L, Jakobsson N. The effect of robot interventions on sleep in adults: a systematic review and network meta-analysis. Journal of Clinical Sleep Medicine. 2022;18(7):1877-1884. doi:10.5664/jcsm.10022
6. 6. Rashid N, Leow Y, Klainin-Yobas P, Itoh S, Wu VX. The effectiveness of a therapeutic robot “Paro”, on behavioural and psychological symptoms, medication use, total sleep time and sociability in older adults. International Journal of Nursing Studies. 2023;145:104530.
7. 7. Pfisterer K, Amelard R, Boger J, Keller H, Chung A, Wong A. Enhancing food intake tracking in long-term care with automated food imaging and nutrient intake tracking (AFINI-T) technology: validation and feasibility assessment. JMIR Aging. 2022;5(4):e37590. doi: 10.2196/37590
8. 8. Kim JH, Lee DS, Kwon SK. Food classification and meal ıntake amount estimation through deep learning. Applied Sciences 2023;13(9):5742. doi: 10.3390/app13095742

9. 9. Papathanail I, Brühlmann J, Vasiloglou MF, et al. Evaluation of a novel artificial intelligence system to monitor and assess energy and macronutrient ıntake in hospitalised older patients. Nutrients. 2021;13(12):4539. doi: 10.3390/nu13124539
10. 10. Amini N, Devriendt A, Lapauw L, et al. Estimating protein intake in sarcopenic older adults: combining food diaries and weighed powders versus 24-hour urine collections. Journal of Nutrition, Health and Aging. 2025;29(3). doi: 10.1016/j.jnha.2024.100474
11. 11. The Ingenious AI That’s Combatting Malnutrition And Optimising Nutrition in Australia | Deloitte Australia. Accessed February 28, 2026. https://www.deloitte.com/au/en/services/consulting/case-studies/ingenious-ai-combatting-malnutrition-optimising-nutrition-australia.html
12. 12. Hermsen S, Mars M, Higgs S, Frost JH, Hermans RCJ. Effects of eating with an augmented fork with vibrotactile feedback on eating rate and body weight: a randomized controlled trial. Int J Behav Nutr Phys Act. 2019;16(1):90. doi: 10.1186/s12966-019-0857-7
13. 13. Bi Y, Lv M, Song C, Xu W, Guan N, Yi W. AutoDietary: A Wearable Acoustic Sensor System for Food Intake Recognition in Daily Life. IEEE Sens J. 2016;16(3).
14. 14. Park T, Mahmud TI, Lee J, et al. A machine-learning-enabled smart neckband for monitoring dietary intake. PNAS Nexus. 2024;3(5). doi: 10.1093/pnasnexus/pgae156
15. 15. Jovanovic M, Mitrov G, Zdravevski E, et al. Ambient assisted living: scoping review of artificial ıntelligence models, domains, technology, and concerns. J Med Internet Res. 2022;24(11):e36553. doi: 10.2196/36553
16. 16. Grammatikopoulou M, Lazarou I, Alepopoulos V, et al. Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study. Front Aging Neurosci. 2024;16:1375131. doi: 10.3389/fnagi.2024.1375131
17. 17. Yeung SSY, Reijnierse EM, Pham VK, et al. Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta‐analysis. J Cachexia Sarcopenia Muscle. 2019;10(3):485. doi: 10.1002/jcsm.12411
18. 18. Ceyhan AB, Altay O, Zhang C, Temel SG, Turkez H, Mardinoglu A. Unravelling the complexity of sarcopenia through a systems biology approach. International Journal of Molecular Science. 2025;26(17):8527. doi: 10.3390/ijms26178527
19. 19. Yi J, Shin YR, Hahn S, Lee YH. Deep learning based sarcopenia prediction from shear-wave ultrasonographic elastography and gray scale ultrasonography of rectus femoris muscle. Scientific Reports. 2022;12(1):3596-. doi: 10.1038/s41598-022-07683-6
20. 20. Cormier H, Rudkowska I, Lemieux S, Couture P, Voh MC. Expression and sequence variants of inflammatory genes; effects on plasma inflammation biomarkers following a 6-week supplementation with fish oil. International Journal of Molecular Sciences. 2016;17(3):375. doi: 10.3390/ijms17030375
21. 21. Calcaterra V, Verduci E, Milanta C, et al. The benefits of the Mediterranean diet on ınflamm-aging in childhood obesity. Nutrients. 2024;16(9):1286. doi: 10.3390/nu16091286
22. 22. de Jong Gierveld J, van Tilburg T. The De Jong Gierveld short scales for emotional and social loneliness: Tested on data from 7 countries in the UN generations and gender surveys. Eur J Ageing. 2010;7(2):121-130. doi: 10.1007/s10433-010-0144-6
23. 23. Yamada Y, Shinkawa K, Nemoto M, Arai T. Automatic assessment of loneliness in older adults using speech analysis on responses to daily life questions. Front Psychiatry. 2021;12:712251. doi: 10.3389/fpsyt.2021.712251
24. 24. Björnwall A, Colombo PE, Sydner YM, Neuman N. The impact of eating alone on food intake and everyday eating routines: A cross-sectional study of community-living 70- to 75-year-olds in Sweden. BMC Public Health. 2024;24(1):2214.
25. 25. Wyman C, Thomas J, Lawless M, Yaxley A. Associations between nutritional and physical outcomes of community-dwelling older adults eating alone, versus with others: A systematic review. Appetite. 2026;217. doi: 10.1016/j.appet.2025.108327
26. 26. Yoo IJ, Park DH, Lee OE, Park A. Investigating older adults’ use of a socially assistive robot via time series clustering and user profiling: descriptive analysis study. JMIR Form Res. 2024;8(1):e41093. doi: 10.2196/41093
27. 27. Yang Y, Wang C, Xiang X, An R. AI applications to reduce loneliness among older adults: a systematic review of effectiveness and technologies. Healthcare. 2025;13(5):446.
28. 28. Lin J, Jiang Y, Wang G, et al. Associations of short sleep duration with appetite-regulating hormones and adipokines: A systematic review and meta-analysis. Obesity Reviews. 2020;21(11):e13051. doi: 10.1111/obr.13051
29. 29. Dichter MN, Dörner J, Wilfling D, et al. Intervention for sleep problems in nursing home residents with dementia: A cluster-randomized study. Int Psychogeriatr. 2024;36(10):965-978. doi: 10.1017/S1041610223004489
30. 30. Tran VP, Al-Jumaily AA, Islam SMS. Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: a comprehensive review. Big Data and Cognitive Computing. 2019;3(1):3. doi: 10.3390/bdcc3010003
31. 31. Siyahani F, Molina GG, Barr S, Mushtaq F. Performance evaluation of a smart bed technology against polysomnography. Sensors (Basel). 2022;22(7):2605.
32. 32. Giggins OM, Doyle J, Hogan K, George M. The impact of a cycled lighting intervention on nursing home residents: a pilot study. Gerontol Geriatr Med. 2019;5:2333721419897453.
33. 33. Baandrup L, Jennum PJ. Effect of a dynamic lighting intervention on circadian rest-activity disturbances in cognitively impaired, older adults living in a nursing home: A proof-of-concept study. Neurobiol Sleep Circadian Rhythms. 2021;11(3):100067.
34. 34. Marziali RA, Franceschetti C, Dinculescu A, et al. Reducing loneliness and social isolation of older adults through voice assistants: literature review and bibliometric analysis. J Med Internet Res. 2024;26(1):e50534. doi: 10.2196/50534
35. 35. Fan W, Zhao R, Liu X, Ge L. Intelligent robot interventions for people with dementia: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res. 2025;27:e59892. doi: 10.2196/59892
36. 36. Broadbent E, Loveys K, Ilan G, et al. ElliQ, an AI-based social robot to alleviate loneliness: progress and lessons learned. JAR life. 2024;13:22. doi: 10.14283/jarlife.2024.2
37. 37. Igarashi T, Iijima K, Nitta K, Chen Y. Detailed analysis of responses from older adults through natural speech: comparison of questions by AI agents and humans. Int J Environ Res Public Health. 2024;21(9):1170. doi: 10.3390/ijerph21091170
38. 38. Barrett MA, Allen A, Vuong VT, et al. Impact of an artificial intelligence and machine learning enhanced electronic health record system on quality measures in nursing homes: a difference-in-differences analysis. Journal of the American Medical Directors Association. 2025;26(7):105680. doi: 10.1016/j.jamda.2025.105680
39. 39. Sapci AH, Sapcı HA. Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: systematic review. JMIR Aging, 2019;2(2):e15429. doi: 10.2196/15429.
40. 40. Gesner E, Dykes PC, Zhang L, Gazarian P. Documentation burden in nursing and its role in clinician burnout syndrome. Appl Clin Inform. 2022;13(5):983.
41. 41. Lapp L, Egan K, McCann L, Mackenzie M, Wales A, Maguire R. Decision support tools in adult long-term care facilities: scoping review. J Med Internet Res. 2022;24(9):e39681.
42. 42. Ye J, Woods D, Jordan N, Starren J. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA Summits on Translational Science Proceedings. 2024;2024:459.
43. 43. Abdellatif A, Bouaud J, Lafuente-Lafuente C, Belmin J, Séroussi B. Computerized decision support systems for nursing homes: a scoping review. J Am Med Dir Assoc. 2021;22(5):984-94. doi: 10.1016/j.jamda.2021.01.080
44. 44. Bucalon B, Shaw T, Brown K, Kay J. State-of-the-art dashboards on clinical ındicator data to support reflection on practice: scoping review. JMIR Med Inform. 2022;10(2):e32695.
45. 45. Kumar D, Suthar N. Predictive analytics and early intervention in healthcare social work: a scoping review. Soc Work Health Care. 2024;63(4-5):208-229.
46. 46. Zheng X, Zeng Z, S van Schooten K, Yang Y. Machine learning approach for frailty detection in long-term care using accelerometer-measured gait and daily physical activity: model development and validation study. JMIR Aging. 2025;8(1):e77140.
47. 47. Ruiz-Ruiz L, Jimenez AR, Garcia-Villamil G, Seco F. Detecting fall risk and frailty in elders with inertial motion sensors: a survey of significant gait parameters. Sensors. 2021;21(20):6918. doi: 10.3390/s21206918
48. 48. González-Castro A, Benítez-Andrades JA, González-González R, Prada-García C, Leirós-Rodríguez R. Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors. Digit Health. 2025;11:20552076251331750.