人工智能在老年照护情绪管理应用中的研究进展

doi:10.3969/j.issn.1003-9198.2026.04.017

Abstract

Abstract: With the acceleration of global aging, emotional health issues among the elderly have become increasingly prominent. Traditional intervention methods have limitations such as strong reliance on drugs and significant side effects. Artificial intelligence (AI) technology, through emotion recognition, dynamic prediction, and personalized intervention, offers innovative solutions for elderly emotional care. This article systematically reviews the current application status, core scenarios, challenges, and future directions of AI in elderly emotional care management, aiming to provide theoretical references and practical guidance for the development of related fields in China. The future development urgently requires a multi-dimensional approach including aging-friendly technological transformation, data and algorithm governance, and policy coordination to truly make AI a partner in elderly emotional care.

Key words: aged, artificial intelligence, care, emotion management, geriatric nursing

CLC Number:

R 473.2

TIAN Shuyun, CHANG Lijuan, CHEN Xin, FAN Qi, MA Feifei. Research progress of artificial intelligence in emotional management for elderly care[J]. Practical Geriatrics, 2026, 40(4): 417-421.

References

[1] 第七次全国人口普查公报(第五号) - 国家统计局[EB/OL].(2021-05-11)[2025-08-25]. https://www.stats.gov.cn/sj/tjgb/rkpcgb/qgrkpcgb/202302/t20230206_1902005.html.
[2] ZHAO K X, HUANG C Q, XIAO Q, et al. Age and risk for depression among the elderly:a meta-analysis of the published literature[J]. CNS Spectr, 2012, 17(3):142-154.
[3] LU L, SHEN H, TAN L, et al. Prevalence and factors associated with anxiety and depression among community-dwelling older adults in Hunan, China:a cross-sectional study[J]. BMC Psychiatry, 2023, 23(1):107.
[4] 国务院办公厅关于印发“十四五”国民健康规划的通知_卫生_中国政府网[EB/OL]. (2022-05-20)[2025-03-23]. https://www.gov.cn/zhengce/content/2022-05/20/content_5691424.htm.
[5] ÖN B I, VIDAL X, BERGER U, et al. Antidepressant use and stroke or mortality risk in the elderly[J]. Eur J Neurol, 2022, 29(2):469-477.
[6] AMENDOLA S, PLÖDERL M, HENGARTNER M P. Suicide rates and prescription of antidepressants[J]. Crisis, 2024, 45(3):225-233.
[7] ESMAEILZADEH P, MIRZAEI T, DHARANIKOTA S. Patients’ perceptions toward human-artificial intelligence interaction in health care:experimental study[J]. J Med Internet Res, 2021, 23(11):e25856.
[8] SHANKAR R, BUNDELE A, MUKHOPADHYAY A. Barriers and enablers for the deployment of large language model-based conversational robots for older adults:a protocol for a systematic review of qualitative studies[J]. PLoS One, 2025, 20(4):e0321093.
[9] TAN C K, LOU V W Q, CHENG C Y M, et al. Improving the social well-being of single older adults using the LOVOT social robot:qualitative phenomenological study[J]. JMIR Hum Factors, 2024, 11:e56669.
[10] CHEN S C, MOYLE W, JONES C, et al. A social robot intervention on depression, loneliness, and quality of life for Taiwanese older adults in long-term care[J]. Int Psychogeriatr, 2020, 32(8):981-991.
[11] DANIELI M, CIULLI T, MOUSAVI S M, et al. Assessing the impact of conversational artificial intelligence in the treatment of stress and anxiety in aging adults:randomized controlled trial[J]. JMIR Ment Health, 2022, 9(9):e38067.
[12] 孙永健. 智能时代数字适老化转型研究[J]. 中国特色社会主义研究, 2025(4):107-119.
[13] HUMAYUN A, MADAWANA A M, HASSAN A, et al. Artificial intelligence as a predictive tool for mental health status:insights from a systematic review and meta-analysis[J]. PLoS One, 2025, 20(9):e0332207.
[14] NOWAKOWSKA K, SAKELLARIOS A, KAŹMIERSKI J, et al. AI-enhanced predictive modeling for identifying depression and delirium in cardiovascular patients scheduled for cardiac surgery[J]. Diagnostics:Basel, 2023, 14(1):67.
[15] CHEN Y M, CHEN P C, LIN W C, et al. Predicting new-onset post-stroke depression from real-world data using machine learning algorithm[J]. Front Psychiatry, 2023, 14:1195586.
[16] CHU C S, WANG D Y, LIANG C K, et al. Automated video analysis of audio-visual approaches to predict and detect mild cognitive impairment and dementia in older adults[J]. J Alzheimers Dis, 2023, 92(3):875-886.
[17] GEORGESCU A L, CUMMINS N, MOLIMPAKIS E, et al. Screening for depression and anxiety using a nonverbal working memory task in a sample of older brazilians:observational study of preliminary artificial intelligence model transferability[J]. JMIR Form Res, 2024, 8:e55856.
[18] LU L C, LAN S H, HSIEH Y P, et al. Effectiveness of companion robot care for dementia:a systematic review and meta-analysis[J]. Innov Aging, 2021, 5(2):igab013.
[19] MOYLE W, ARNAUTOVSKA U, OWNSWORTH T, et al. Potential of telepresence robots to enhance social connectedness in older adults with dementia:an integrative review of feasibility[J]. Int Psychogeriatr, 2017, 29(12):1951-1964.
[20] CHOI J, LEE S, KIM S, et al. Depressed mood prediction of elderly people with a wearable band[J]. Sensors:Basel, 2022, 22(11):4174.
[21] BYEON H. Exploring factors for predicting anxiety disorders of the elderly living alone in South Korea using interpretable machine learning:a population-based study[J]. Int J Environ Res Public Health, 2021, 18(14):7625.
[22] CHU C H, LESLIE K, SHI J, et al. Ageism and artificial intelligence:protocol for a scoping review[J]. JMIR Res Protoc, 2022, 11(6):e33211.
[23] CHIN M H, AFSAR-MANESH N, BIERMAN A S, et al. Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care[J]. JAMA Netw Open, 2023, 6(12):e2345050.
[24] CHU C H, NYRUP R, LESLIE K, et al. Digital ageism:challenges and opportunities in artificial intelligence for older adults[J]. Gerontologist, 2022, 62(7):947-955.
[25] CHAR D S, SHAH N H, MAGNUS D. Implementing machine learning in health care - addressing ethical challenges[J]. N Engl J Med, 2018, 378(11):981-983.
[26] CHEN Y, CLAYTON E W, NOVAK L L, et al. Human-centered design to address biases in artificial intelligence[J]. J Med Internet Res, 2023, 25:e43251.
[27] MURDOCH B. Privacy and artificial intelligence:challenges for protecting health information in a new era[J]. BMC Med Ethics, 2021, 22(1):122.
[28] 潘燕桃, 汪庆怡. 数智技术对老年群体数智素养的挑战及其对策研究[J]. 图书馆杂志, 2025, 44(6):36-44.
[29] WANG Y, ZENG H, LV F, et al. Analysis of demand and influencing factors for smart senior care among older adults in underdeveloped regions of western China:a case study of Lanzhou[J]. Front Public Health, 2024, 12:1337584.
[30] GUDALA M, ROSS M E T, MOGALLA S, et al. Benefits of, barriers to, and needs for an artificial intelligence-powered medication information voice chatbot for older adults:interview study with geriatrics experts[J]. JMIR Aging, 2022, 5(2):e32169.
[31] BANDINI A, REZAEI S, GUARIN D L, et al. A new dataset for facial motion analysis in individuals with neurological disorders[J]. IEEE J Biomed Health Inform, 2021, 25(4):1111-1119.
[32] CHOI N. Relationship between health service use and health information technology use among older adults:analysis of the US National Health Interview Survey[J]. J Med Internet Res, 2011, 13(2):e33.
[33] DERMODY G, FRITZ R, GLASS C, et al. Factors influencing community-dwelling older adults’ readiness to adopt smart home technology:a qualitative exploratory study[J]. J Adv Nurs, 2021, 77(12):4847-4861.
[34] OKUN S, AYALON L. And what about self-ageism? “inner work” as a fifth strategy for the eradication of ageism[J]. J Aging Soc Policy, 2024, 36(5):732-748.
[35] TSAI H S, SHILLAIR R, COTTEN S R, et al. Getting grandma online:are tablets the answer for increasing digital inclusion for older adults in the U.S.?[J]. Educ Gerontol, 2015, 41(10):695-709.
[36] SINGH R P, HOM G L, ABRAMOFF M D, et al. Current challenges and barriers to real-world artificial intelligence adoption for the healthcare system, provider, and the patient[J]. Transl Vis Sci Technol, 2020, 9(2):45.
[37] WANG J, WANG Y, CAI H, et al. Analysis of the status quo of the elderly’s demands of medical and elderly care combination in the underdeveloped regions of Western China and its influencing factors:a case study of Lanzhou[J]. BMC Geriatr, 2020, 20(1):338.

Research progress of artificial intelligence in emotional management for elderly care

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Recommended 10

[1]	JI Junjie, ZHOU Zhonghan, LIU Gongyue, LIU Yixi, LIAO Limin. Correlation between frailty and the risk of benign prostatic hyperplasia/lower urinary tract symptoms in elderly males aged 60 and above in China [J]. Practical Geriatrics, 2026, 40(4): 325-331.
[2]	MO Sisi, YU Yanlan. Analysis of factors related to therapeutic efficacy of sacral nerve modulation in elderly patients with neurogenic lower urinary tract dysfunction [J]. Practical Geriatrics, 2026, 40(4): 340-343.
[3]	LU Shuang, LI Xia, REN Qianmeng. Risk prediction model of cognitive dysfunction in elderly patients with Parkinson’s disease based on quantitative MRI parameters [J]. Practical Geriatrics, 2026, 40(4): 362-366.
[4]	XU Zhongmei, LIU Fang, CHANG Yun, ZHENG Xuemei, SHANG Shuhua. Influence of sleep duration and social isolation on cognitive frailty in elderly patients with chronic heart failure [J]. Practical Geriatrics, 2026, 40(4): 377-381.
[5]	LIU Wen, LIU Yuting, JI Muhuo. T cell mitochondrial dysfunction link to postoperative delirium in elderly thoracic surgery patients [J]. Practical Geriatrics, 2026, 40(4): 382-388.
[6]	MENG Hanlu, SHEN Qiuyue, CHEN Junjun, CHEN Chen, XU Lili, GUO Qingyu, CHEN Min. Effect of multi-dimensional collaborative management based on frailty risk assessment on blood glucose control and health promotion behavior in elderly patients with T2DM complicated with frailty risk [J]. Practical Geriatrics, 2026, 40(4): 394-399.
[7]	QI Rongfu, XIE Tong, ZHOU Jie, ZHOU Liwu, YIN Haijian. Early application value of high tibial osteotomy combined with platelet-rich plasma in the treatment of elderly patients with medial compartment osteoarthritis [J]. Practical Geriatrics, 2026, 40(4): 400-405.
[8]	YANG Yao, WAN Meiping, ZHANG Wenbin, LUO Yuanrong. Construction and validation of a nomogram for predicting delayed recovery of neurocognitive function after deep brain stimulation in elderly patients with Parkinson’s disease [J]. Practical Geriatrics, 2026, 40(4): 411-416.
[9]	SHI Chaoxiong, QIAN Lijun, LU Miao, PANG Sisi, WANG Sen, XIA Lili, XIAO Suwan, LI Tingru, TANG Yifan. Summary of the best evidence for polypharmacy management in elderly patients with chronic heart failure [J]. Practical Geriatrics, 2026, 40(4): 427-432.
[10]	LIU Yukun, LEI Jiawei, ZHU Chunming, ZHANG Yuqiang, WANG Jiahe. Research progress on molecular mechanism and drug target of fraity syndrome [J]. Practical Geriatrics, 2026, 40(3): 224-228.
[11]	SUN Kexin, LEI Jiawei, ZHANG Guilei, YANG Zhenrong, WANG Jiahe. Research progress on drug targets for the treatment of sarcopenia in the elderly [J]. Practical Geriatrics, 2026, 40(3): 229-233.
[12]	LI Minglin, LI Minfeng, ZENG Sanlong, LIANG Qingjing, LIU Jiye, LUO Jianwen. Research advances in drug targets for visual impairment in the elderly [J]. Practical Geriatrics, 2026, 40(3): 234-240.
[13]	XIAO Lingyi, XIAO Guojin, LIAO Shunqi, HOU Suying, LIU Lixin, XIA Lin. Insulin nasal drops for the prevention of delirium after surgery in elderly patients: a meta-analysis [J]. Practical Geriatrics, 2026, 40(3): 254-260.
[14]	XU Jiaping, HE Chao, LIN Xiaofeng, ZHANG Lixia, ZHU Zixian, KONG Li. Construction and validation of an in-hospital hypothermia risk prediction model for elderly patients with severe trauma in Emergency Department [J]. Practical Geriatrics, 2026, 40(3): 261-266.
[15]	ZHAO Xia, WANG Dongxu, LAI Xiaoxing, HUO Xiaopeng. Analysis of oral frailty prevalence and its influencing factors in hospitalized elderly patients with comorbidities [J]. Practical Geriatrics, 2026, 40(3): 267-271.