Emotion Recognition, Loneliness, Long-term Care Facility, Music Therapy, Negative Affect
Conditions
Keywords
Music Therapy, Long-Term Care Facility, Emotion Recognition, Emotional Health
Brief summary
The goal of this pilot study is to learn whether group music therapy improves the emotional health of residents living in long-term care facilities. It will also examine the feasibility of integrating an artificial intelligence (AI)-based emotion recognition model into routine psychosocial interventions. The main questions it aims to answer are: Does group music therapy improve positive affect and reduce negative affect, depression, and loneliness among long-term care residents? Are AI-based facial emotion recognition results consistent with residents' self-reported emotional assessments? Researchers will use a one-group pretest-posttest quasi-experimental design to evaluate changes before and after a 6-week group music therapy program. The study will also compare subjective questionnaire results with objective facial emotion recognition outputs generated by the PaLI Gemma 2 multimodal model. Participants will: Attend one 60-minute group music therapy session per week for 6 weeks Complete emotional health questionnaires before the first session and after the sixth session Be recorded during sessions using a non-invasive camera system for facial emotion analysis Have their questionnaire results compared with AI-based emotion recognition outputs to evaluate consistency and feasibility This pilot study will provide preliminary evidence regarding both the psychological benefits of group music therapy and the feasibility of applying AI-supported multimodal emotion assessment in long-term care settings.
Detailed description
Taiwan is entering a super-aged society, and an increasing number of older adults reside in long-term care (LTC) facilities. Although institutional care provides medical and daily living support, many residents experience loneliness, depressive symptoms, and reduced emotional well-being. Non-pharmacological interventions such as group music therapy have demonstrated beneficial effects on mood regulation, social interaction, and psychological health. However, most previous studies have relied primarily on self-report questionnaires or observer-rated scales, which may be influenced by cognitive status, expressive ability, or assessment bias. This pilot study aims to evaluate both the psychological effects of group music therapy and the feasibility of integrating an artificial intelligence (AI)-based multimodal emotion recognition model into emotional assessment in LTC settings. Study Design This study uses a quasi-experimental one-group pretest-posttest design. Approximately 20 residents from a long-term care facility in central Taiwan will be recruited using convenience sampling. Inclusion criteria include: Age 65 years or older Residency in the facility for at least 3 months Possesses basic cognitive functions (0-2 errors on the SPMSQ scale) Ability to participate in group activities Provision of informed consent for participation and facial image collection Residents with severe dementia, major psychiatric disorders, neurodegenerative diseases, severe sensory impairments, or recent participation in other psychological or music therapy programs will be excluded. Intervention Participants will receive a 6-week group music therapy program. Each participant will attend one 60-minute session per week, for a total of 6 sessions. Sessions will be conducted in small groups (approximately 10 participants per group) and led by a trained music therapist with support from nursing staff and research personnel. Each session will include: Music listening using culturally familiar songs Instrument interaction (e.g., tambourines, hand bells, xylophones) Singing and vocal expression activities Short group sharing discussions to promote emotional expression and social connection The intervention is designed to progressively enhance emotional engagement, social interaction, and psychological comfort in a supportive group environment. Outcome Measures Emotional health outcomes will be assessed at two time points: T0 (baseline): Before the first session T1 (post-intervention): Within 24 hours after the sixth session Subjective measures include: International Positive and Negative Affect Schedule Short Form (I-PANAS-SF) Geriatric Depression Scale - 15 item version (GDS-15) UCLA Loneliness Scale Version 3 Barthel Index (functional status, baseline only) AI-Based Multimodal Emotion Analysis During each music therapy session, participants' facial images will be captured using a non-invasive camera system under standardized environmental conditions. The PaLI Gemma 2 multimodal model will analyze static facial images to generate: Categorical emotion outputs (e.g., happy, sad, angry, fear, surprise, disgust, neutral) Continuous emotion dimensions (valence and arousal values) Emotion recognition outputs from Session 1 (baseline representation) and Session 6 (post-intervention representation) will be compared with corresponding subjective questionnaire scores. Correlation analyses (Pearson or Spearman) will examine the consistency and complementary value between AI-based emotion detection and self-reported emotional states. Statistical Analysis Descriptive statistics will summarize demographic and baseline characteristics. Pre-post differences in emotional outcomes will be examined using: Paired t-tests for normally distributed data Wilcoxon signed-rank tests for non-normally distributed data Effect sizes (Cohen's dz or r) and 95% confidence intervals will be reported to support future sample size estimation for larger trials. Correlation analyses will evaluate agreement between subjective measures and AI-derived emotional indicators. Ethical Considerations The study will be approved by an Institutional Review Board (IRB) prior to implementation. Written informed consent will be obtained from all participants. Facial image data will be anonymized and used solely for emotion recognition analysis. No identity recognition will be performed. All data will be securely stored in encrypted databases, and participants may withdraw at any time without affecting their care. Significance This pilot study will provide preliminary evidence regarding: The effectiveness of group music therapy in improving emotional well-being among LTC residents. The feasibility of integrating AI-based multimodal emotion assessment into long-term care practice. Findings will inform the development of future large-scale trials and contribute to the advancement of technology-assisted psychosocial care models in aging societ
Interventions
OTHERGroup Music Therapy
This intervention consists of a structured 6-week group music therapy program designed for residents of a long-term care facility. Participants attend one 60-minute session per week, for a total of six sessions. Sessions are conducted in small groups (approximately 10 participants per group) and are led by a trained music therapist with support from nursing staff. The intervention follows an active music therapy approach and includes four core components: (1) listening to culturally familiar music to evoke emotional resonance; (2) interactive instrument play using simple percussion instruments to promote engagement and coordination; (3) group singing and vocal expression to facilitate emotional expression and social bonding; and (4) brief group sharing discussions to encourage reflection and interpersonal connection. The program is progressively structured to enhance emotional engagement and social interaction in a supportive environment. In addition to standard psychosocial outcome
Sponsors
Yu Chia Chang
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Intervention model description
This study uses a single-arm, quasi-experimental, one-group pretest-posttest interventional design. All enrolled participants will receive the same 6-week group music therapy intervention. Emotional health outcomes will be measured before the intervention (baseline, T0) and after completion of the intervention (post-intervention, T1). There is no control or comparison group in this pilot study. The primary purpose is to evaluate preliminary intervention effects and assess feasibility, including the integration of AI-based facial emotion recognition with subjective self-report measures. The study is designed as an early-phase feasibility and proof-of-concept investigation to generate effect size estimates and methodological insights for future randomized controlled trials.
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
Aged 65 years or older Resident of the long-term care facility for at least 3 months SPMSQ scale error 0-2 questions Medically stable and able to participate in group activities Able to communicate and complete questionnaires (with assistance if needed) Willing to provide written informed consent, including consent for facial image collection
Exclusion criteria
Diagnosed with severe dementia Diagnosed with major psychiatric disorders Diagnosed with neurodegenerative diseases that significantly impair participation Severe hearing or visual impairment that prevents engagement in music therapy activities Participation in other psychological therapy or music therapy programs within the past 3 months Acute medical condition requiring hospitalization or intensive treatment
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in Positive Affect Score on the I-PANAS-SF | Baseline, Week 6 | Assessed using the short form of the International Positive and Negative Affect Schedule (I-PANAS-SF). The positive affect subscale consists of 5 items rated on a 5-point Likert scale (1=never, 5=always). Total subscale scores range from 5 to 25, with higher scores indicating stronger positive emotional intensity. |
| Change in Negative Affect Score on the I-PANAS-SF | Baseline, Week 6 | Assessed using the short form of the International Positive and Negative Affect Schedule (I-PANAS-SF). The negative affect subscale consists of 5 items rated on a 5-point Likert scale (1=never, 5=always). Total subscale scores range from 5 to 25, with higher scores indicating stronger negative emotional intensity. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Geriatric Depression Scale | Baseline, Week 6 | Depressive symptoms will be assessed using the 15-item Geriatric Depression Scale (GDS-15). It is a yes/no self-report instrument. Total scores range from 0 to 15, with higher scores indicating greater severity of depressive symptoms. |
| UCLA Loneliness Scale | Baseline and Week 6 | The complete scale consists of 20 items, using a four-point scale (1 = never, 4 = always), with a total score range of 20-80 points. The score intervals are defined as follows: 20-34 points for low-level loneliness, 35-49 points for moderate loneliness, 50-64 points for high-level loneliness, and 65-80 points for severe loneliness. |
Countries
Taiwan
Outcome results
None listed