研究背景
由於經濟發展及醫療科技的進步，國人的預期壽命日漸提升，台灣已邁入高齡社會。人口老化將造成社會、經濟以及醫療的沉重負擔，因此如何使長者健康老化，是刻不容緩的重要課題，而降低年齡歧視可能有助於長者健康老化。
「年齡歧視」的定義為「基於年齡針對他人或自己的刻板印象（我們如何思考）、偏見（我們如何感受）和歧視（我們如何行動）」（世界衛生組織，2021）。年齡歧視也可能表現在制度及人際層面，或是針對自己，並且有正向及負向的年齡歧視表現。年齡歧視對於健康造成的負面影響，已有許多研究探討及證實。Chang等人於2020年發表的一篇系統性文獻回顧，納入全球六大洲、45個國家的422篇研究，結果發現共有95.5%的研究顯示年齡歧視與11個心理、認知及身體健康具有負面相關性，其中心理層面的影響機轉可能是年齡歧視經驗所造成的負面情緒導致。由Kim等人於2015年發表的研究中，提到年齡歧視對於憂鬱症的影響，會經由憤怒、悲傷以及無助等年齡歧視經歷相關負面情緒完全中介。因此本研究欲探討年齡歧視和健康的關係，以及年齡歧視和情緒反應的關係。
情緒的表現方式有許多種，包含臉部表情、聲音、肢體動作以及身體接觸等。情緒表現的功能包含：為環境挑戰做出適應性的反應以及向他人傳達重要的社會資訊。其中由於臉部表情富含情緒資訊及易於分類辨識的特性，是目前最廣為使用的情緒表現研究主題。由Ekman等學者於1978年提出的臉部動作編碼系統(Facial Action Coding System; FACS)將情緒分為六大基本情緒：快樂、驚喜、恐懼、厭惡、憤怒和悲傷。由於科技的進步，透過電腦軟體的輔助，研究者能得到即時且細緻的臉部表情情緒資料。本研究使用的FaceReader軟體運用人工智慧及深度神經網絡學習，能夠依照臉部動作編碼系統精準辨識20個臉部動作單元，進而辨識六個基本情緒，且經由實驗證實具有高度的正確率(91%)。由於情緒往往並非一閃而逝，而是隨著時間波動和變化的，因此使用電腦軟體將能更精準地辨識這些情緒變化。引發情緒的方式有許多種，包含聲音、文字及影像等，本研究透過情境影片的方式能夠提供一致的刺激，確保引發情緒研究的可信度。

研究目的
1. 探討長者觀看年齡歧視情境影片的臉部表情反應與其年齡歧視問卷結果的關係。
2. 探討長者的年齡歧視問卷結果與其健康的關係。

研究方法
本研究招募南台灣65歲以上的社區長者，收集基本人口學變項資料（年齡、性別、婚姻狀況、教育程度、受試地點、慢性疾病和多重用藥與否），並使用對老年人的態度和想法量表(Attitude and Thoughts Toward Older People Scale; AT-TOPS)來測量長者的年齡歧視觀念和態度（包含制度、人際及自我導向年齡歧視），以老年憂鬱量表(5-item Geriatric Depression Scale; GDS-5)測量長者心理健康，使用簡易心智狀態問卷(Short Portable Mental Status Questionnaire; SPMSQ)測量長者認知健康，並以高齡者生理復原力量表(Physical Resilience Instrument for Older Adults; PRIFOR)、基本日常生活功能量表(Katz Index of Independence in Activities of Daily Living; Katz ADL)、工具性日常生活功能量表(Instrumental Activities of Daily Living Scale; IADLS)、長者健康整合式功能評估(Integrated Care for Older People Screening Tool for Taiwanese; ICOPES-TW)、簡易身體表現功能量表(Short Physical Performance Battery; SPPB)、五次起立坐下測試、跌倒風險和身體組成分析測量長者身體健康，探討長者年齡歧視問卷結果與其健康的關係。並且讓長者觀看年齡歧視情境影片，使用FaceReader臉部表情辨識軟體測量情緒反應，探討長者臉部表情反應與其年齡歧視問卷結果的關係。

研究結果
在本研究納入的61位參與者中，平均年齡（±標準差）為74.50 ± 6.50，女性佔多數(n = 50, 82.0%)，對老年人的態度和想法量表(AT-TOPS)的得分（±標準差）分別為：制度年齡歧視18.15 ± 3.37、人際年齡歧視18.28 ± 4.59、自我導向年齡歧視14.70 ± 4.28，整體對老年人的態度和想法量表(AT-TOPS)得分（±標準差）為51.13 ± 10.44。在心理健康方面，參與者的老年憂鬱量表(GDS-5)得分（±標準差）為1.54 ± 0.96；在認知健康方面，簡易心智狀態問卷(SPMSQ)得分（±標準差）為9.31 ± 1.54，其他身體健康相關量表得分（±標準差）如下：高齡者生理復原力量表(PRIFOR)為54.52 ± 10.08，基本日常生活功能量表(Katz ADL)為13.77 ± 1.06，工具性日常生活功能量表(IADLS)為9.38 ± 3.53，長者健康整合式功能評估(ICOPES-TW)為1.46 ± 1.29。在三種情境影片（正向、中性與負向）中，快樂(Happiness)為出現時長最高的情緒，其次為悲傷(Sadness)。值得注意的是，在負向情境中，厭惡(Disgust)情緒的出現時長時間最長，佔總時間的22.38% ± 29.01%。
對老年人的態度和想法量表(AT-TOPS)整體分數與制度、人際及自我導向年齡歧視分數皆呈現顯著正向關聯，制度年齡歧視和高齡者生理復原力量表(PRIFOR)呈顯著負向關聯，而自我導向年齡歧視則和老年憂鬱量表(GDS-5)及長者健康整合式功能評估(ICOPES-TW)量表分數（其中較高分數代表較差的內在能力）呈顯著正向關聯，此外整體分數與慢性疾病及長者健康整合式功能評估(ICOPES-TW)量表分數呈顯著正向關聯。並且在控制年齡與性別變項後，在負向情境中制度年齡歧視和情緒比例1（正向情緒快樂與驚喜的平均值除以負向情緒悲傷、憤怒、厭惡與恐懼的平均值）呈顯著負相關(β = -0.201, SE = 0.102, p = 0.049)。而人際年齡歧視與情緒比例2（正向情緒快樂與驚喜的平均值除以負向情緒悲傷、憤怒與厭惡的平均值）及情緒比例3（正向情緒快樂除以負向情緒悲傷、憤怒與厭惡的平均值）皆呈顯著下降，然而自我導向年齡歧視與對老年人的態度和想法量表(AT-TOPS)整體分數與所有本研究中所測量的情緒比例之間並未發現統計上顯著相關。
關於年齡歧視與其他健康結果之迴歸分析結果，在控制年齡與性別變項後，制度年齡歧視與高齡者生理復原力量表(PRIFOR)呈顯著負相關(B = -0.823, SEb = 0.360, β = -0.087, p = 0.022)，並與長者健康整合式功能評估(ICOPES-TW)分數(B = 0.078, SEb = 0.026, β = 0.041, p = 0.003)呈顯著正相關。而人際年齡歧視和簡易身體表現功能量表(SPPB)呈顯著負相關(B = -0.222, SEb = 0.104, β = -0.089, p = 0.032)。自我導向年齡歧視及對老年人的態度和想法量表(AT-TOPS)整體分數雖與簡易心智狀態問卷(SPMSQ)呈現負相關，但此相關性未達統計上顯著。

研究結論
本研究結果與過去文獻在年齡歧視對健康影響的證據一致，發現年齡歧視與長者的心理、認知與身體健康負面結果有關聯。此外本研究結果亦指出，與年齡歧視相關的情境影片（尤其是負向年齡歧視之情境）可能會引發負向情緒反應。有鑑於此，年齡歧視對於長者健康以及情緒的潛在影響需要更多大眾及未來研究的關注和重視，而降低對於長者的年齡歧視更是刻不容緩的重要議題，或許可以結合政策例如透過社區營造或是心理衛生教育等策略。

Background
Due to sustained economic growth and advancements in healthcare, Taiwan had experienced a steady increase in life expectancy, officially entering the status of an aged society. The rapid aging of the population had led to substantial social, economic and healthcare burdens. Therefore, promoting healthy aging among older adults became an urgent and critical public issue, and reducing ageism might serve as a key strategy to support this goal.
According to the World Health Organization (WHO, 2021), ageism was defined as the stereotyping, prejudice, and discrimination against individuals or groups based on their age. It comprised three types: stereotypes (how we think), prejudice (how we feel), and discrimination (how we act). Ageism could be conceptualized at three levels, including institutional, interpersonal, and self-directed. Moreover, it encompassed both negative and positive ageism. A growing body of research demonstrated that ageism was associated with negative health outcomes.
Chang et al. (2020) conducted a systematic review revealing that ageism was prevalent in all 45 countries across six continents where studies were conducted. Among the 422 studies reviewed, 95.5% reported that ageism had detrimental effects. It was significantly associated with poorer outcomes across 11 psychological, cognitive, and physical health domains. The psychological pathway might have been related to emotional responses. Kim et al. (2015) reported that the effect of ageism on depression was fully mediated by negative emotions related to experiences of ageism, including anger, sadness, and helplessness. Therefore, the present study aimed to examine the relationship between ageism and health, as well as the relationship between ageism and emotional responses.
Emotion could be expressed through various modalities, including facial expressions, vocalizations, bodily movements, and touch. Emotional expressions were believed to have evolved to serve two primary functions: (1) preparing the organism to respond adaptively to recurrent environmental challenges, and (2) communicating essential social information to others. Among these modalities, facial expression was identified as one of the richest and most distinguishable sources of emotional information. Consequently, most studies investigating emotional expression focused on facial expressions.
In 1978, Ekman developed the Facial Action Coding System (FACS), which categorized facial muscle movements associated with six basic emotions: happiness, surprise, fear, disgust, anger and sadness. With the advancement of facial expression recognition software, researchers were able to capture real-time and specific emotional responses. In the present study, FaceReader software utilized a deep artificial neural network to identify 20 facial action units and recognize six basic emotions, achieving high accuracy in emotion recognition—up to 91%.
Importantly, emotions were not merely flash-like reactions; they unfolded dynamically and fluctuated over time. The use of computer software allowed for more precise detection of dynamic emotional responses. Emotional responses could be elicited through various stimuli, including sounds, words, and films. Furthermore, vignettes offered a standardized context that minimized bias and facilitated consistent emotional induction across participants.

Objectives
1. To examine the relationships between facial expression recognition in response to ageism scenario videos and ageism questionnaire responses among older adults.
2. To examine the relationships between ageism questionnaire responses and health outcomes among older adults.

Methods
This cross-sectional study recruited community-dwelling older adults aged 65 and above in southern Taiwan. Demographic factors collected included age, sex, marriage, education, survey site, chronic disease and polypharmacy. Ageism was assessed using the Attitude and Thoughts Toward Older People Scale (AT-TOPS), which encompassed three types: institutional, interpersonal and self-directed ageism. Psychological health was measured by the 5-item Geriatric Depression Scale (GDS-5), while cognitive health was evaluated using the Short Portable Mental Status Questionnaire (SPMSQ). Physical health was assessed through multiple measurements, including the Physical Resilience Instrument for Older Adults (PRIFOR), Katz Index of Independence in Activities of Daily Living (Katz ADL), Instrumental Activities of Daily Living Scale (IADLS), Integrated Care for Older People Screening Tool for Taiwanese (ICOPES-TW), Short Physical Performance Battery (SPPB), five-time chair stand test, fall risk, and body composition. The study aimed to examine the associations between ageism questionnaire responses and health outcomes among older adults. Additionally, participants viewed ageism scenario videos, during which their emotional responses were recorded and analyzed using FaceReader facial expression recognition software. The study further explored the relationships between facial expressions and ageism questionnaire responses.

Results
Of the 61 eligible participants, the mean (± SD) age was 74.50 ± 6.50 years, and the majority were female (n = 50, 82.0%). AT-TOPS mean (± SD) scores were 18.15 ± 3.37 (institutional ageism), 18.28 ± 4.59 (interpersonal ageism), and 14.70 ± 4.28 (self-directed ageism). Additionally, the overall AT-TOPS mean (± SD) score was 51.13 ± 10.44. For mental health outcomes, the participants reported mean (± SD) GDS-5 scores of 1.54 ± 0.96. For cognitive health outcomes, the mean (± SD) SPMSQ score was 9.31 ± 1.54. And other physical health outcomes as follows: 54.52 ± 10.08 (PRIFOR), 13.77 ± 1.06 (Katz ADL), 9.38 ± 3.53 (IADLS) and 1.46 ± 1.29 (ICOPES-TW). Across the three scenarios (i.e., positive, neutral, and negative), happiness was the most prevalent emotion across all three scenarios, followed by sadness. Notably, disgust had the longest activated period in the negative scenario (22.38% ± 29.01%).
The results showed significant positive associations between the overall AT-TOPS score and all three types of ageism. Institutional ageism was significantly and negatively associated with PRIFOR, and self-directed ageism was significantly and positively associated with both GDS-5 and ICOPES-TW scores (in which higher scores indicate poorer intrinsic capacity). Additionally, the overall AT-TOPS score was significantly and positively associated with both chronic disease and ICOPES-TW scores. After controlling for age and sex, the institutional ageism was significantly associated with reduced emotion Ratio 1 (Positive (Happiness + Surprise) ÷2/Negative (Sadness + Anger + Disgust + Fear) ÷4) in the negative scenario (β = -0.201, SE = 0.102, p = 0.049). And the interpersonal ageism was significantly associated with reduced Ratio 2 (Positive (Happiness + Surprise) ÷2/Negative (Sadness + Anger + Disgust) ÷3) and Ratio 3 (Positive (Happiness)/Negative (Sadness + Anger + Disgust) ÷3). However, no statistically significant relationship was found between the self-directed ageism or overall AT-TOPS score and all emotion ratios in this study.
As for the regression results of ageism and other health outcomes, after controlling for age and sex, the institutional ageism was significantly associated with reduced PRIFOR (B = -0.823, SEb = 0.360, β = -0.087, p = 0.022) and increased ICOPES-TW (B = 0.078, SEb = 0.026, β = 0.041, p = 0.003). And the interpersonal ageism was significantly associated with reduced SPPB (B = -0.222, SEb = 0.104, β = -0.089, p = 0.032). The self-directed ageism and the overall AT-TOPS score were associated with reduced SPMSQ, while there was no statistically significant difference.

Conclusion
Consistent with previous research, the current study found that ageism was associated with negative psychological, cognitive, and physical health outcomes among older people. Moreover, ageism scenario videos, particularly the negative ageism scenario, appeared to elicit negative emotional responses. These results highlighted the importance of raising public awareness about ageism and emphasized the need for further research. Moreover, they underscored the urgent priority of reducing ageism in society. Policy interventions might include strategies such as community engagement or mental health education.

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