Associations Between Physical Features and Behavioral Patterns in Macau Outdoor Community Public Spaces and Older Adults’ Performance of Instrumental Activities of Daily Living

Abstract

Objective: This exploratory study examines potential associations between the physical features and behavioral patterns of outdoor community public spaces and the Instrumental Activities of Daily Living (IADLs) performance of older adults in Macau. IADLs refer to abilities that reflect functional independence and cognitive capacity. Methods: Nine representative public spaces were selected in Macau. Field measurements of spatial features, non-participant behavioral observations, and standardized IADL assessments were conducted. Spearman’s correlation and multiple regression analyses were employed to examine relationships among environmental factors, observed behaviors, and IADL scores. Variable selection is based on theoretical support and statistical methods, including correlation analysis and Variance Inflation Factor (VIF) diagnostics. Results: Findings suggest that a higher visible greenery ratio and a greater density of resting facilities are positively associated with IADL performance. Conversely, frequent engagement in sedentary activities, such as playing board or card games, appears to be linked to lower functional independence. Conclusions: This study highlights possible associations between public space characteristics and older adults’ functional independence. The results underscore the need to further explore how spatial design and behavioral patterns may relate to aging in urban environments. Enhancing green visibility and increasing resting facilities could potentially support functional independence, whereas reducing prolonged sedentary behaviors may also be beneficial. These insights offer preliminary guidance for policymakers and urban planners aiming to develop more age-friendly environments in high-density cities.

1. Introduction

Global aging presents challenges for urban planning, social policy, and healthcare, with the population aged 65 and above expected to increase from 10% in 2022 to 16% by 2050 [1]. As the aging population grows, cities must rethink the built environment to accommodate the physical, social, and psychological needs of older adults [2]. Among the key concerns arising from this demographic shift is the need to enhance the health and quality of life of older adults, particularly through the promotion of independent living and cognitive status [3,4,5]. Instrumental Activities of Daily Living (IADLs) are critical indicators of independence and cognitive capacity [6], with associations to cognitive status [5]. These activities, encompassing tasks such as shopping, transportation, cooking, and financial management, require a combination of physical, cognitive, and environmental adaptability [7]. Declines in IADL performance are often early indicators of deteriorating health or cognitive function, making IADLs a vital focus of gerontological research and public health initiatives [8,9].

While much existing research has primarily focused on the relationship between cognitive decline and the inability to perform IADLs [9,10], a significant gap remains in understanding the extent to which external environmental factors, particularly the physical features and behaviors within public spaces, may be associated with older adults’ ability to perform these tasks. Although individual health factors and disease processes are widely recognized as the most direct determinants of IADLs [11], external environmental factors likely play a critical, though often underestimated, role in older adults’ IADL performance. This gap is particularly relevant in the context of aging in place, where the focus has shifted from solely within the home to include the broader community [12,13,14,15].

Community public spaces are essential venues for older adults to engage in physical activity, social interaction, and recreational behaviors, all of which contribute to their overall well-being. Existing studies have highlighted the importance of the built environment in promoting the health and well-being of older adults, suggesting that the physical features and behaviors of outdoor public spaces can significantly influence older adults’ physical health [16,17,18,19]. However, despite these insights, the exact extent to which physical features and behavioral patterns within public spaces affect IADL performance in older adults remains largely unexplored. To what extent do specific features and behaviors within these spaces contribute to the maintenance or improvement in IADLs in older adults? The question remains unanswered but it is essential for a fuller understanding of the role that external environments play in the cognitive health and independence of aging populations. By addressing this gap, this study aims to examine and clarify the potential associations of public space features and behaviors on older adults’ IADL performance, providing a deeper understanding of how environmental factors interact with personal health in influencing daily living. Using on-site measurements, behavioral observations, and IADL assessments, this study provides insights for designing age-friendly public spaces. Ultimately, this research bridges the gap between IADL-focused healthcare studies and environmental design, contributing to the development of inclusive public spaces that enhance older adults’ independence and quality of life.

2. Literature Review

The review categorizes research into four sections: (1) IADLs in Aging Research, (2) Factors Associated with IADL Performance in Older Adults, (3) Environmental Factors and Behaviors in Public Spaces, and (4) Research Gaps and Directions. This organization highlights the importance of investigating the influence of public space physical features and behavioral patterns on IADL performance in older adults, underscoring the need for further investigation into these areas.

2.1. IADLs in Aging Research

IADLs are key indicators of independence; declines often signal deteriorating health or cognition [20]. It has been shown to play a significant role in enhancing older adults’ sense of autonomy and self-esteem. A study by Bleijenberg [8] found that older adults who retained the ability to perform IADLs reported higher levels of well-being and satisfaction with life. A Japanese study further highlighted that maintaining IADL abilities allows older adults to engage more actively in community activities, thereby improving both physical independence and mental health [21]. The connection between IADLs and independence is also supported by studies examining environmental factors. For example, a cohort study conducted in Florianopolis, Brazil, found that urban environments with certain characteristics were associated with the incidence of disability in both basic and Instrumental Activities of Daily Living in older adults, promoting functional independence and better IADL performance [22]. This suggests that physical environments significantly influence older adults’ ability to perform daily tasks, further underlining the importance of IADLs for independent living. Thus, understanding the interaction between external environmental factors and IADL performance is critical for supporting aging in place.

2.2. Factors Associated with IADL Performance in Older Adults

IADL performance is influenced by various factors, including demographic, health, environmental, and psychological determinants. Age and gender are primary contributors, with aging and gender differences often correlating with declining IADL performance due to factors like increased chronic illnesses and cognitive decline [23,24]. Disease burden and chronic diseases, including cardiovascular and metabolic disorders, significantly exacerbate IADL limitations, particularly when coupled with cognitive impairments such as dementia and mild cognitive decline [25,26]. The nutritional status of older adults plays an essential role in their ability to perform IADLs. Malnutrition and dehydration significantly impair energy levels, cognition, and physical strength, which are essential for executing tasks like grocery shopping or managing medications [27,28]. Physical activity and mental health are closely linked to IADL performance, with regular exercise improving both mobility and cognition, while mental health conditions such as depression can impair IADL performance [29]. Furthermore, social support plays a crucial role in maintaining IADL independence, where limited social networks are associated with increased difficulty in performing IADLs. Social engagement, conversely, supports independence and functional capacity [30,31,32]. In addition, factors such as socioeconomic status, education, cultural background, and familiarity with technology can further impact an individual’s ability to perform IADLs [33,34,35,36].

However, environmental and built environment factors are particularly influential in shaping IADL performance. Poor street conditions, such as the absence of crosswalks and uneven surfaces, increase the risk of losing IADL abilities, especially in those already experiencing functional impairments [37]. Neighborhoods with greater safety were associated with a reduced likelihood of IADL decline, while physical features such as streetscapes, pathways, and lighting had a stronger association with IADL limitations rather than basic ADL loss [38]. Additionally, poor neighborhood cleanliness and high levels of disorder are linked to higher rates of IADL decline [39], while neighborhood cleanliness was found to specifically correlate with IADL performance [40]. Moreover, exposure to green spaces has been shown to improve physical functioning and reduce both ADL and IADL loss, thereby lowering the burden of long-term care [41]. These findings highlight the critical role of environmental factors, such as safety, street conditions, and green space access, in shaping older adults’ capacity to maintain independence through IADLs.

Despite these insights, research systematically linking public space features and behaviors to IADL performance remains limited. Most existing research on environmental impacts on aging has addressed general functional disabilities or ADL loss, rather than explicitly examining the relationships between specific public space features, older adults’ behavioral patterns, and IADL performance. This research gap presents an opportunity to further investigate how public space physical features are associated with the ability of older adults to perform IADLs. Understanding these connections could inform the development of public spaces that better support the independence and well-being of the aging population.

2.3. Environmental Factors and Behaviors in Public Spaces

Older adults prioritize physical comfort and environmental conditions [42]. Choi indicated that promoting age-friendly communities and supporting aging in place should prioritize enhancing the built environment as a foundation for fostering an age-friendly social environment [43]. Older adults’ behaviors in public spaces differ due to specific spatial needs [44]. Understanding senior behaviors is crucial for rethinking urban strategies aimed at creating more age-friendly environments [45]. Studies on older adults’ behavior in public spaces suggest that features such as seating facilities, green spaces, and safe pathways can facilitate greater mobility, social interaction, and engagement in physical activity [46]. Furthermore, public spaces that encourage social interaction have been shown to improve IADL performance by fostering community engagement and reducing social isolation [32].

Nevertheless, current research rarely explores how specific public space features directly relate to IADL performance. Moreover, although the broader relationship between environmental factors, behavioral patterns, and aging is well-documented, the specific impacts of social behaviors and interactions in public spaces on older adults’ IADL performance remain underexplored.

2.4. Research Gaps and Directions

The review above identifies several critical research gaps. First, while the existing literature clearly associates various personal, health-related, and environmental determinants with older adults’ overall functional abilities, few studies explicitly investigate the direct relationships between specific physical features of public spaces—such as resting facilities, visible greenery, and spatial enclosure—and older adults’ IADL performance. Second, although behaviors occurring within public spaces, including social interactions and physical activities, are acknowledged as beneficial, the extent to which these specific behaviors contribute to maintaining or improving IADL performance remains underexplored. Thus, there is a need for integrated empirical studies combining precise physical measurements of public space features, detailed observational analysis of older adults’ behavioral patterns, and standardized assessments of IADL performance. By exploring how distinct spatial features and behavioral patterns within public spaces might specifically relate to older adults’ IADL capacities, this study aims to address these research gaps comprehensively. Such integrated analyses will inform urban design practices and policies aimed at creating supportive public environments that enhance older adults’ functional independence and overall quality of life.

3. Materials and Methods

3.1. Research Area and Sample Selection

This study selected nine public spaces in Macau based on their representativeness, diversity, and research feasibility, with the aim of investigating how different outdoor environments influence aging in place and IADL performance. The selection criteria considered factors such as the concentration of elderly residents, cultural and historical context, spatial scale, and functional diversity. Particular emphasis was placed on spaces that are frequently used by older adults, especially those located in districts with a high density of elderly populations, as these environments play a vital role in supporting daily routines, physical activity, and social engagement. The nine selected sites were grouped into three categories based on their cultural characteristics and degree of embeddedness in everyday urban life in Macau: (1) Chinese, (2) Portuguese, and (3) Sino-Portuguese integrated public spaces. This classification reflects key differences in place meaning, social configuration, environmental atmosphere, and levels of local integration, which are critical for interpreting behavioral outcomes and contextualizing elderly spatial experiences in a historically layered city like Macau. Although the spaces differ in size, their selection was guided by the medium-scale characteristics typical of Macau’s largos, which are generally considered suitable for the everyday use of the elderly population.

The spatial distribution and cultural categorization of the nine sites are presented in Figure 1, which maps their locations across various urban districts and typologies. In addition to locational data, Figure 1 includes comparative weekday and weekend observations of the proportion of older adult users in each space. This information helps to quantify site-specific elderly engagement and supports the empirical relevance of each site for analyzing aging-in-place behavior across temporal dimensions.

3.2. Composition of Physical Features in Community Public Spaces

The physical features of Macau community public spaces are selected based on the concept of aging in place [3,4], with a focus on how the characteristics of public spaces can support older adults’ ability to live independently and engage in daily activities. To structure the analysis, the study draws on a scoping review of place attachment and aging [47], identifying three key dimensions of public space that contribute to aging in place: Spatial Features, Natural Features, and Elder-friendly and Accessible Features (see

Table 1. Quantification methods for the physical features of community public spaces (source: authors).

	Environmental Features	Quantification Method	Data Sources	Reference
Spatial Features	Area	Measure the total area of the space in square meters.	Field measurements	[48]
	Percentage of Ground Pavement	The pavement ratio refers to the proportion of the area covered by building materials such as concrete, ceramic tiles, floor tiles, cast-in-place grass bricks, and embossed flooring, relative to the total floor area of the sample space.	Field measurements	[49]
	Spatial Enclosure	Measure the ratio of the enclosing boundary length to the perimeter of the space.	Field measurements	[50]
	Spatial Visual Brightness	the perceived brightness of a space, taking into account the distribution and intensity of light across the area. Measure the luminance and record the luminance values at each point. Represented by the standard deviation of luminance.	Sampling calculation	[51]
Natural Features	Green Coverage Ratio	Measure the proportion of greenery in the space relative to total area.	Field measurements	[52]
	Visible greenery ratio	Calculate the proportion of visible green space within a given area.	Sampling calculation	[53]
	Plant Diversity	Plant diversity (R) was calculated using the Patrick index, where R = S, with S representing the number of plant species in the sample area. To account for variations in the size of the sample space, the formula was adjusted to R = S/lgA, where A is the total area of the sample space.	Field measurements	[54]
	Green Space Ratio	The proportion of green spaces within the sample area—such as ground-level areas suitable for planting trees, shrubs, flower beds, lawns, and groundcover plants, which are used for ecological functions or public recreation—relative to the total site area. Field measurement involves comparing the area of these green spaces to the overall site area.	Field measurements	[55]
Elder-friendly and Accessible Features	Functional Diversity	Functional diversity (F) was calculated using the Patrick index, where F = N, with N representing the number of different facility types within the sample area. Similar to plant diversity, the formula was adjusted to F = N/lgA, where A denotes the total area of the sample space, to account for differences in size.	Field measurements	[56]
	Recreational Facilities	Count the number of recreational facilities (e.g., benches, playgrounds, fitness stations). This indicator is calculated in units of “groups”.	Field measurements	[57]
	Density of Resting Facilities	Measure the number of resting facilities per unit area.	Field measurements	[58]
	Pathway Connectivity	The Connectivity Index is calculated by dividing the number of entrances (E) by the total perimeter (P) of the public space, using the formula C = E/Lg P. To measure, count the entrances and measure the perimeter, then apply the formula—the higher the value, the better the connectivity.	Field measurements	[59]

). By examining these three features, the study aims to identify and understand how the physical environment of public spaces in Macau can facilitate IADL performance and may shape their engagement in outdoor activities during aging in place. Table 1 outlines the quantification methods for various environmental elements based on the three core features. Each method was designed to ensure accurate and consistent measurements of the physical features. The sources and references used for these measurements further substantiate the reliability of the methods employed. All physical features were measured through direct field surveys, supplemented where appropriate by local geospatial data. For example, Spatial Enclosure was calculated by combining GIS data from the “Macau Online Map” with on-site verification to determine the ratio between enclosing boundary length and total perimeter. Plant Diversity was derived from direct botanical observation and species counting in the field, followed by application of the Patrick index, which adjusts for site area variation. The measurement results are shown in

Table 2. Physical features of nine community public spaces in Macau.

		Largo do Pagode do Bazar	Flower City Park	Triangular da Areia Preta Garden	Largo do Lilau	Largo Camoes	Lou Lim loc Park	Largo de Santo Agostinho	Praca de Luis de Camoes	Largo de Se
Spatial Features	Area (m2)	676	5806	1449	576	827	3928	515	4874	1260
	Percentage of Ground Pavement (%)	91.00	36.30	63.67	75.74	69.24	19.34	64.66	63.74	76.43
	Spatial Enclosure (%)	44.44	37.22	7.96	59.10	84.01	27.8	32.32	66.44	78.36
	Spatial Visual Brightness	64.23	61.45	62.01	61.65	65.93	69.36	60.52	69.89	55.96
Natural Features	Green Space Ratio (%)	7.90	35.73	25.80	15.97	5.39	51.16	20.19	31.6	10.16
	Visible greenery ratio (%)	29.33	50.87	41.33	31.29	24.04	43.49	16.64	45.42	7.63
	Plant Diversity	1.06	4.25	2.53	1.81	2.06	5.56	1.48	2.71	0.97
	Green Coverage Ratio (%)	27.50	42.69	86.40	82.51	47.57	75.46	26.58	45.65	12.27
Elder-friendly and Accessible Features	Functional Diversity	0.35	1.86	1.27	0.72	0.69	1.67	1.48	1.63	0.65
	Recreational Facilities (m2)	32	66	84	15	14	18	9	104	8
	Density of Resting Facilities	0.047	0.011	0.058	0.026	0.017	0.005	0.017	0.021	0.006
	Pathway Connectivity	1.52	1.19	2.63	3.01	1.37	1.13	1.00	1.64	2.27

.

3.3. Behaviors of Older Adults

Older adults’ behaviors were categorized into necessary, optional, and social activities based on Gehl’s framework [60]. This study focuses on optional and social behaviors, with optional activities further classified into static and dynamic types. Activities were excluded following the rationale proposed by Gehl and Svarre [61], as these actions are generally less responsive to variations in spatial design and offer limited insight into how physical environments influence voluntary and social use. The behavioral analysis commenced with coding specific action types observed in the field, as follows:

• Social activities refer to interpersonal interactions occurring in public spaces and include conversing, caring for a child, social gathering, playing board or card games, walking together, participating in community volunteer activities, and attending opera or cultural performances. These subcategories were identified through in situ observation and reflect the typological diversity commonly documented in public life studies. While categorized under a general “social” umbrella for analytical consistency, these behaviors involve varying degrees of interactional depth and social meaning, a nuanced acknowledgment, and are recommended for future theoretical refinement;
• Optional static activities involve minimal physical movement and include sitting, eating or drinking, sunbathing, reading newspapers or books, listening to music or watching performances, using a mobile phone, making phone calls, taking photos, viewing natural landscapes, sleeping, and smoking;
• Optional dynamic activities require physical movement and include walking, fitness walking, jogging, exercising alone, using fitness equipment, walking a dog, singing opera or performing traditional arts, engaging in gardening activities, fetching water, and cleaning.

Behaviors of older adults are recorded through non-participant observation, and the six most frequently occurring behavioral categories in each public space are selected to characterize the behavioral patterns of older adults in that specific environment.

3.4. Data Collection and IADL Assessment

The inclusion criteria for participants were (1) aged 65 or older, (2) local older adults in Macau who have used the selected public spaces; (3) able to respond to the IADL assessment; and (4) voluntary consent to participate. Observations were conducted over a one-month period, during which data collection was distributed across multiple days to constitute a total of four full days of observation. This timeframe aligns with established short-term protocols in public space behavioral research [61,62,63]. Although effective for identifying typical behavioral patterns, longer observation periods may offer greater insight into seasonal or exceptional variations. To ensure temporal variation in behavior, data collection took place across five distinct time slots each day: 08:30–10:30, 10:30–12:30, 14:30–16:30, 16:30–18:30, and 19:30–21:30. Only behaviors sustained for at least five minutes were included, in order to capture meaningful engagements and exclude incidental or transient activities. While this threshold enhances consistency, it may overlook shorter-duration yet purposeful actions such as jogging or brisk walking, particularly in spatially constrained settings. Future studies may consider adjusting observation thresholds based on activity type and spatial context to better reflect the diversity of older adults’ behaviors. The observed behaviors were categorized into the three mentioned types.

For the IADL assessment, older adults engaging in activities within the selected public spaces were randomly invited to participate in an interview or complete a questionnaire based on the IADL scale during December 2024. This scale evaluates an individual’s ability to perform a range of daily tasks, including using the telephone, cooking, housekeeping, laundry, shopping, transportation, medication management, and financial management (see

Table 3. IADL disability levels and scoring criteria *.

Item	Score	Content
A. Ability to Use Telephone	3	Operates telephone on own initiative—looks up and dials numbers, etc.
	2	Dials a few well-known numbers.
	1	Answers telephone but does not dial.
	0	Does not use telephone at all.
B. Shopping	3	Takes care of all shopping needs independently.
	2	Shops independently for small purchases.
	1	Needs to be accompanied on any shopping trip.
	0	Completely unable to shop.
C. Food Preparation	3	Plans, prepares and serves adequate meals independently.
	2	Prepares adequate meals if supplied with ingredients.
	1	Heats, serves and prepares meals, or prepares meals but does not maintain an adequate diet.
	0	Needs to have meals prepared and served.
D. Housekeeping	4	Maintains house alone or with occasional assistance (e.g., “heavy work domestic help”).
	3	Performs light daily tasks such as dishwashing, bed making.
	2	Performs light daily tasks but cannot maintain an acceptable level of cleanliness.
	1	Needs help with all home maintenance tasks.
	0	Does not participate in any housekeeping tasks.
E. Laundry	3	Does personal laundry completely.
	2	Launders small items independently but may require assistance with larger laundry tasks.
	1	Can only handle small laundry tasks but cannot manage more significant laundry tasks independently.
	0	All laundry must be done by others.
F. Mode of Transportation	4	Travels independently on public transportation or drives own car.
	3	Arranges own travel via taxi, but does not otherwise use public transportation.
	2	Travels on public transportation when accompanied by another.
	1	Travel limited to taxi or automobile with assistance of another.
	0	Does not travel at all.
G. Responsibility for Own Medications	2	Is responsible for taking medication in correct dosages at correct times.
	1	Takes responsibility if medication is prepared in advance in separate dosages.
	0	Is not capable of dispensing own medication.
H. Ability to Handle Finances	2	Manages financial matters independently (budgets, writes checks, pays rent, bills, goes to bank), collects and keeps track of income.
	1	Manages day-to-day purchases, but needs help with banking, major purchases, etc.
	0	Incapable of handling money.
Disability Level	IADL Score Range	Description
Level 1	24	No Disability: Fully independent in all IADL tasks (e.g., phone use, shopping, cooking, household chores). No assistance or reminders needed.
Level 2	21–23	Mild Disability: Mostly independent, with occasional minor assistance required (e.g., for shopping or medication).
Level 3	18–20	Mild to Moderate Disability: Independent in most tasks, but needs occasional assistance or reminders (e.g., cooking, shopping, financial management).
Level 4	15–17	Moderate Disability: Requires assistance for most IADL tasks, especially shopping, cooking, and financial management.
Level 5	12–14	Moderate to Severe Disability: Cannot perform many IADL tasks independently and requires regular or full assistance.
Level 6	9–11	Severe Disability: Needs full-time assistance for most IADL activities; unable to independently manage basic daily tasks.
Level 7	6–8	Very Severe Disability: Almost entirely dependent, requiring comprehensive care for all daily activities.
Level 8	0–5	Complete Disability: Totally dependent on others for all activities; requires long-term care with 24-h supervision.

* This table is adopted from the Lawton IADL Scale and the Chinese Taiwanese government’s “Long-term Care Services Subsidy Measures for Disabled Elderly”.

). It is adopted from the Lawton IADL Scale and the China Taiwanese government’s “Long-term Care Services Subsidy Measures for Disabled Elderly.” The Lawton scale is widely recognized for assessing elderly functional abilities, ensuring comparability, while the Taiwanese model is relevant to Macau’s elderly population due to shared cultural and regional factors. IADL scores range from 0 to 24, reflecting levels of independence, with higher scores indicating greater functional ability. Table 3 shows IADL disability levels and scoring. Given that older men in Macau also engage in cooking, housekeeping, and laundry, the same assessment items were used for both male and female participants. Although the primary objective of this study is to obtain IADL assessment scores, efforts were made to minimize behavioral discrepancies among participants by ensuring a balanced distribution of behavioral types, based on the results of non-participant behavioral observation in public spaces. To ensure data representativeness, approximately four participants per time slot were recruited for IADL assessment in each public space.

3.5. Statistical Analysis

Based on the physical characteristics data of public spaces, behavioral data, and IADL statistical data, descriptive statistical analysis was first performed, such as calculating the frequency distribution of behavioral data to understand the basic characteristics of the data. Next, Spearman’s rank correlation analysis was used to identify the independent variables that were significantly correlated with the dependent variable and IADL score and to select the most strongly correlated variables. Only variables with statistically significant correlations (p < 0.05) to IADL scores were selected as independent variables for regression modeling, in order to maintain model parsimony and avoid overfitting. Subsequently, multiple linear regression analysis was conducted, and collinearity diagnostics were performed using the Variance Inflation Factor (VIF) to identify potential redundant variables. Finally, the regression model was optimized by removing variables with severe multicollinearity, and the model was re-adjusted. The relationship of each independent variable on the IADL score was evaluated using regression coefficients and p-values. All statistical analyses were performed with IBM SPSS Statistics (version 26; IBM Corp., Armonk, NY, USA) to ensure the accuracy and reliability of the results.

4. Results

4.1. Percentage of Occurrence for Different Kinds of Behaviors in Nine Sample Spaces

Behavioral data from nine spaces show variation in activity types (see

Table 4. Statistical analysis of the percentage of occurrence for different behaviors.

Behavioral Types		Working Day	Weekend	Frequency	Occurrence Percentage
Optional static activities	Sitting	182	225	407	20.63%
	Eating or Drinking	25	17	42	2.13%
	Sunbathing	17	12	29	1.47%
	Reading Newspapers or Books	1	1	2	0.10%
	Listening to Music or Watching Performances	2	2	4	0.20%
	Using a Mobile Phone	52	72	124	6.28%
	Making Phone Calls	4	4	8	0.41%
	Taking Photos	5	2	7	0.35%
	Viewing Natural Landscapes	2	0	2	0.10%
	Sleeping	3	3	6	0.30%
	Smoking	6	14	20	1.01%
Optional dynamic activities	Walking	75	75	150	7.60%
	Fitness Walking	11	22	33	1.67%
	Jogging	1	1	2	0.10%
	Exercising Alone	48	52	100	5.07%
	Using Fitness Equipment	6	13	19	0.96%
	Walking a Dog	2	0	2	0.10%
	Singing Opera or Performing Traditional Arts	1	0	1	0.05%
	Fetching Water	1	0	1	0.05%
	Cleaning	4	2	6	0.30%
Social activities	Conversing	201	160	361	18.30%
	Caring for a Child	25	7	32	1.62%
	Social Gathering	20	17	37	1.88%
	Playing Board or Card Games	250	265	515	26.10%
	Walking Together	14	0	14	0.71%
	Participating in Community Volunteer Activities	8	1	9	0.46%
	Attending Opera or Cultural Gatherings	17	23	40	2.03%

). Among the optional static activities, sitting (20.63%) was the most common static activity, followed by using a mobile phone (6.28%) and eating or drinking (2.13%). When it comes to optional dynamic activities, walking emerged as the most prevalent behavior, occurring in 7.6% of the observed spaces. Exercising alone followed closely with 4.58% of occurrences. Social activities represented a significant portion of the behaviors observed in these spaces, particularly conversing (18.3%) and playing board or card games (26.10%). These activities highlight the importance of interaction and communal engagement among older adults in public spaces. The high frequency of these behaviors indicates that these spaces serve as important social hubs for older adults, facilitating their need for connection and leisure.

Therefore, the occurrence probabilities of six behaviors, like playing board or card games, conversing, sitting, walking, using a mobile phone, and exercising alone, will be used to represent the behavioral patterns of the selected public spaces (see

Table 5. Behavioral activity distribution across selected public spaces.

	Sitting	Using a Mobile Phone		Walking	Exercising Alone	Conversing	Playing Board or Card Games
SPACE 1	Largo do Pagode do Bazar	2.4%	10.2%	10.3%	0.0%	5.3%	0.0%
SPACE 2	Flower City Park	9.5%	8.2%	8.8%	2.3%	7.4%	2.4%
SPACE 3	Triangular da Areia Preta Garden	32.5%	26.5%	16.2%	9.3%	35.3%	73.6%
SPACE 4	Largo do Lilau	6.5%	6.1%	0.0%	2.3%	7.9%	0.0%
SPACE 5	Largo Camoes	1.2%	0.0%	1.5%	0.0%	3.2%	0.0%
SPACE 6	Lou Lim loc Park	9.5%	18.4%	23.5%	53.5%	14.2%	0.0%
SPACE 7	Largo de Santo Agostinho	0.6%	2.0%	0.0%	2.3%	3.2%	0.0%
SPACE 8	Praca de Luis de Camoes	33.1%	22.4%	36.8%	25.6%	23.7%	24.0%
SPACE 9	Largo de Se	4.7%	6.1%	2.9%	4.7%	0.0%	0.0%

). For accuracy, this study adopts a ranking system to replace the raw behavior data with ordinal values: “High = 3”, “Medium = 2”, and “Low = 1”. This approach converts the continuous variables into ordered variables, which will be adapted for the physical features of public space and used for subsequent data analysis. The reason for converting continuous variables of spatial and behavioral data into ordinal variables is to address the uncertainty and randomness within the data, thereby better reflecting general trends in reality. Through this ranking system, the overall impact of spatial forms and behavioral characteristics on IADL scores can be identified, rather than relying solely on the specific values of individual sample spaces. This approach enables a deeper understanding of the potential role of different spatial and behavioral features on older adults’ IADL performance.

4.2. Associations of Physical Features and Behavioral Patterns on IADL Scores

The IADL assessment collected a total of 289 samples from 9 public spaces (see

Table 6. Frequency distribution of participants across public spaces.

	Frequency	Percent
Flower City Park	37	12.8
Largo Camoes	31	10.7
Largo de Santo Agostinho	19	6.6
Largo de Se	18	6.2
Largo do Lilau	33	11.4
Largo do Pagode do Bazar	40	13.8
Lou Lim loc Park	31	10.7
Praca de Luis de Camoes	40	13.8
Triangular da Areia Preta Garden	40	13.8
Total	289	100.0

and

Table 7. Distribution of IADL scores and frequency.

IADL Score	Frequency	Percent	Cumulative Percent
7.00	1	0.3	0.3
10.00	1	0.3	0.7
11.00	2	0.7	1.4
12.00	8	2.8	4.2
13.00	3	1.0	5.2
14.00	7	2.4	7.6
15.00	11	3.8	11.4
16.00	15	5.2	16.6
17.00	14	4.8	21.5
18.00	22	7.6	29.1
19.00	30	10.4	39.4
20.00	28	9.7	49.1
21.00	34	11.8	60.9
22.00	44	15.2	76.1
23.00	41	14.2	90.3
24.00	28	9.7	100.0
Total	289	100.0
Mean	19.85
Median	21

). Among these participants, 78.5% scored 18 or higher, with a mean IADL score of 19.85. These results indicate that most older adults active in public spaces have an IADL rating above LEVEL3. Moreover, the median score of 21, corresponding to LEVEL 2, suggests a relatively high level of independence.

Spearman’s rank correlation was performed to examine the relationships between various physical features, behaviors, and IADL scores (see

Table 8. Correlation analysis between physical features, behavior, and IADL scores.

		IADL_Score
		Pearson Correlation	Sig. (2-Tailed)
1	Area (m2)	0.118 *	0.045
2	Percentage of Ground Pavement (%)	−0.125 *	0.034
3	Spatial Enclosure (%)	−0.140 *	0.017
4	Green Space Ratio (%)	0.129 *	0.029
5	Visible greenery ratio (%)	0.239 **	0.000
6	Recreational Facilities (m2)	0.251 **	0.000
7	Density of Resting Facilities	0.211 **	0.000
8	Conversing	0.211 **	0.000
9	Playing Board or Card Games	0.153 **	0.009
10	Pathway Connectivity	0.103	0.081
11	Using a Mobile Phone	0.102	0.082
12	Walking	0.102	0.082
13	Functional Diversity	0.101	0.087
14	Plant Diversity	0.099	0.092
15	Sitting	0.094	0.111
16	Green Coverage Ratio (%)	0.05	0.399
17	Spatial Visual Brightness	0.043	0.472
18	Exercising Alone	−0.021	0.719

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

). The results revealed several correlations:

(1)

Visible greenery ratio and recreational facilities showed strong positive correlations with IADL scores (r = 0.239, r = 0.251, p < 0.001);

(2)

The density of resting facilities also demonstrated a positive correlation (r = 0.211, p < 0.001), suggesting that spaces with more resting facilities promote better IADL outcomes;

(3)

Conversing (r = 0.211, p < 0.001) and playing board or card games (r = 0.153, p = 0.009) were positively correlated with IADL scores.

In contrast, Spatial Enclosure and Percentage of Ground Pavement exhibited weak negative correlations with IADL scores (r = −0.140 and r = −0.125, respectively; p < 0.05), suggesting that spaces with higher enclosure or more paved surfaces may be less conducive to maintaining functional independence in older adults.

The low r value suggests that the model may not have explained all the variations in the IADL scores, possibly because IADLs are influenced by multiple factors, including other unaccounted variables such as the most significant factors: personal health and chronic diseases, among others. The statistically significant p-value of less than 0.05 indicates that there is indeed a relationship between these independent variables and IADL scores, further demonstrating the contribution of these variables in predicting IADL scores. Subsequently, a multiple linear regression analysis was performed to evaluate the influence of the independent variables on the IADL score. The initial regression model yielded an R² value of 0.136, but several variables exhibited high Variance Inflation Factors (VIFs), suggesting substantial multicollinearity. Specifically, the variables Area (m²) (VIF = 54.196), Green Space Ratio (%) (VIF = 61.777), Recreational Facilities (m²) (VIF = 74.075), and Percentage of Ground Pavement (%) (VIF = 32.335) exhibited excessive multicollinearity. To address this, these four variables were removed from the model, and a revised regression analysis was conducted.

A refined regression analysis was subsequently conducted. The adjusted model, incorporating five predictors, yielded an R² of 0.125 and an adjusted R² of 0.109, indicating that approximately 10.9% of the variance in IADL scores can be explained by the retained variables (see

Table 9. Initial model and optimized model summary.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
Initial model	0.368 a	0.136	0.111	3.105
	a. Predictors: (Constant), Playing Board or Card Games, Spatial Enclosure (%), Density of Resting Facilities, Visible Greenery Ratio (%), Conversing, Recreational Facilities (m2), Area (m2), Green Space Ratio (%)
Optimized model	0.353 b	0.125	0.109	3.107
	b. Predictors: (Constant), Playing Board or Card Games, Spatial Enclosure (%), Density of Resting Facilities, Visible Greenery Ratio (%), Conversing

and

Table 10. Optimized multiple linear regression results for IADL score prediction.

	Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Correlations			Collinearity Statistics
	B	Std. Error	Beta			Zero-Order	Partial	Part	Tolerance	VIF
(Constant)	17.903	0.951		18.827	0.000
Spatial Enclosure (%)	−0.251	0.156	−0.111	−1.605	0.110	−0.140	−0.095	−0.089	0.650	1.538
Visible greenery ratio (%)	1.161	0.269	0.499	4.311	0.000	0.239	0.248	0.240	0.231	4.325
Density of Resting Facilities	0.737	0.163	0.341	4.520	0.000	0.211	0.259	0.251	0.544	1.839
Conversing	−0.870	0.495	−0.204	−1.758	0.080	0.211	−0.104	−0.098	0.229	4.374
Playing Board or Card Games	−0.944	0.354	−0.252	−2.663	0.008	0.153	−0.156	−0.148	0.345	2.897

). The model was statistically significant (F = 8.079, p < 0.001), and all remaining VIF values were below five, confirming that multicollinearity had been effectively addressed. Although the R² value decreased slightly from 0.136 to 0.125 following the removal of multicollinear variables, the resulting model is more parsimonious and statistically robust. This trade-off enhances the interpretability and stability of the regression coefficients, thereby improving the reliability of the findings.

(a)

The optimized regression model identified several significant predictors of IADL scores;

(b)

Visible Greenery Ratio (%): Exhibited the strongest positive association with IADL scores (B = 1.161, β = 0.499, p < 0.001), suggesting that greater visual access to green spaces is significantly linked to higher functional independence;

(c)

Density of Resting Facilities: Also showed a significant positive effect (B = 0.737, β = 0.341, p < 0.001), indicating that an increased availability of resting areas is beneficial for maintaining independence;

(d)

Playing Board or Card Games: Revealed a significant negative effect on IADL scores (B = −0.944, β = −0.252, p = 0.008), which implies that frequent participation in such sedentary social activities may be associated with reduced functional independence;

(e)

Conversing: Although it showed a negative relationship with IADL scores (B = −0.870, β = −0.204, p = 0.080), this effect did not reach statistical significance;

(f)

Spatial Enclosure (%): Had a minor negative impact (B = −0.251, β = −0.111, p = 0.110), but this relationship was not statistically significant.

Overall, the results suggest that green space visibility and the availability of resting facilities are positively linked to IADL performance, while sedentary social activities such as board or card games may contribute to reduced functional independence. Other factors, including spatial enclosure and conversing, did not show significant effects in this model.

5. Discussion

This section interprets the key empirical findings on how public space features and behavioral patterns are associated with older adults’ IADL performance. It evaluates the implications of these results in relation to prior research, highlighting theoretical contributions, practical relevance, limitations, and directions for future inquiry.

5.1. Exploratory Insights into the Associations Between Physical Features, Behavioral Patterns, and IADL Performance

This study aimed to explore the associations between physical features and behavioral patterns in community public spaces and their relationship with IADL performance in older adults in Macau. The results provide valuable insights but also highlight the complexity of the relationship between the physical environment and IADL performance. IADL performance is influenced by multiple factors, including health status, cognitive ability, social support, and so on. The relatively low Pearson correlation coefficients (r values between −0.3 and 0.3) and the R² value of 0.125 in the regression model suggest that while there are statistically significant relationships between certain physical features, behavioral patterns, and IADL scores, these factors alone explain only a small portion of the variation in IADL performance: approximately 10.9%. These findings align with prior research acknowledging that IADL performance is a multifaceted issue influenced by a range of internal and external factors [64]. This relatively low explanatory power indicates a limitation in capturing the full complexity of IADL performance through environmental and behavioral variables alone. Prior studies have also reported limited effect sizes in similar contexts. For instance, Saelens and Handy [65] noted that built environment attributes typically account for a small proportion of variance in walking behavior, and Ewing and Cervero [66] observed similar patterns in travel behavior studies. The results support the hypothesis that the physical environment does play a role in supporting older adults’ independence but also reinforce the need for a more comprehensive understanding of the factors at play. The significant predictors identified still provide valuable insights for policy and urban design, demonstrating that even incremental improvements in the built environment can have meaningful impacts on older adults’ independence and cognitive well-being.

5.2. Key Observations on Visible Greenery Ratio, Resting Facilities, and Sedentary Activities

This study identified notable associations between physical space features, behaviors, and IADL performance. One of the most significant findings was the positive association between the visible greenery ratio and IADL scores, supporting the notion that natural elements in public spaces foster mental restoration and physical engagement. Previous studies have emphasized that greenery supports not only psychological well-being but also promotes outdoor activity [41,52], which in turn can preserve functional capacities and other daily tasks [21]. In this context, green visibility serves as a facilitator for cognitive health, which is critical for IADL performance. Natural elements, therefore, play a direct role in promoting independence by supporting cognitive recovery and engagement in functional activities.

The density of resting facilities correlated positively with IADL scores, supporting previous research on the importance of seating areas [67]. These spaces help reduce fatigue, allowing older adults to stay outdoors longer and engage in activities such as walking, shopping, and socializing—critical tasks for maintaining independence. The presence of resting areas also allows for a balanced pacing of activities, helping older adults mitigate physical strain and increase their ability to perform IADLs [22]. In this way, resting facilities function as crucial transitional spaces in the public environment, directly contributing to older adults’ functional independence.

In contrast, there is an observed negative correlation between playing board or card games and IADL performance. Although these activities offer cognitive and social benefits, their sedentary nature may limit physical engagement and contribute to a decline in physical function, especially when they dominate older adults’ routines [68]. While these games can foster cognitive engagement and social bonding, their predominantly sedentary nature may limit opportunities for the moderate physical exertion that supports performing IADL tasks. Thus, although social activities generally have beneficial psychosocial and cognitive aspects, an overemphasis on sedentary pursuits could inadvertently undermine physical robustness and daily functional capacity.

The underlying mechanisms through which physical features and behavioral patterns may contribute to IADL performance can be understood through a more integrated perspective that considers the interaction of cognitive, physical, and behavioral dynamics. In the study, green visibility and resting facilities are not only correlated with IADL performance but may also play a role in IADL completion by supporting cognitive recovery and reducing fatigue. Similarly, social activities such as playing board games indirectly may reflect functional decline. These findings suggest that behavioral choices in public spaces are influenced by individual health conditions, underscoring the complexity of these relationships.

It is important to recognize that the interaction between the built environment and individual health factors complicates direct interpretations of the relationship with IADL performance. While visible greenery and resting areas may contribute to cognitive and physical well-being, their potential benefits could vary depending on individuals’ health conditions, such as chronic illnesses, cognitive impairments, or mobility limitations. These factors may moderate the extent to which older adults benefit from environmental features, highlighting the need for a more integrated approach that considers both environmental design and personal health when planning for functional independence in older adults. This complexity suggests that the built environment interacts with personal health and activity choices in ways that are not fully captured by surface-level correlations.

5.3. Variations in the Impact of Static, Dynamic, and Social Activities on IADL Performance

IADL tasks require both physical and cognitive abilities [33]. Social interactions stimulate cognitive function and maintain mental agility, which are crucial for completing complex daily tasks. Although conversing showed a positive correlation in simple analyses, its effect may be overshadowed by other cognitively related variables, such as playing board or card games, in multivariate regression models. Additionally, the quality and depth of conversations vary greatly in different contexts, potentially reducing their statistical significance when other factors are controlled for. While playing board or card games can promote cognitive exchange in social interactions, they are inherently sedentary and lack the necessary physical activity. Over-involvement in such activities may lead to insufficient physical exercise, negatively associated with IADL tasks that require physical endurance. Therefore, frequent engagement in these activities is associated with lower IADL scores, reflecting a decline in functional independence that prompts individuals with reduced functionality to choose such low-intensity activities.

This study highlights the limitations of static and dynamic activities. Optional static activities such as sitting or using a mobile phone may facilitate rest or information acquisition to some extent, but they generally do not provide enough physical or social stimulation to directly enhance or maintain IADL performance. Prolonged sitting may result in muscle atrophy and endurance decline, but the frequency and duration of sitting or phone use in our sample may not have reached thresholds that may impact functional independence. While optional dynamic activities like walking and exercising may enhance physical health, their direct influence on IADLs is less clear, as IADLs also require cognitive processing, decision-making, and multitasking. These findings suggest that activity choices may reflect individuals’ functional status, with those experiencing greater functional decline being more inclined to engage in sedentary behaviors.

5.4. Policy and Design Implications for Aging in Place in High-Density Asian Cities

This study highlights several actionable insights for enhancing aging in place strategies in high-density Asian cities. Findings from Macau indicate that even subtle adjustments to public space design, such as improving greenery visibility and increasing the availability of resting facilities, may support older adults’ functional engagement and independence.

Visible greenery was found to be positively associated with IADL performance, echoing prior research on its restorative and mobility-supportive effects [41,69]. In compact urban settings, micro-greening strategies like vertical planting and pocket parks can serve as effective interventions. Similarly, a higher density of resting points contributes not only to physical support but also to social connection and spatial inclusion [59,67]. These elements should be integrated into transit nodes, walking routes, and gathering areas frequented by older adults. At the same time, this study cautions that not all social activities are equally beneficial. Frequent sedentary engagement, particularly low-intensity games, may be associated with functional decline. Urban designs should therefore encourage environments that foster both social interaction and light physical activity.

These implications align with the WHO’s age-friendly cities framework, emphasizing accessible, inclusive, and health-supportive spaces [15]. Embedding empirical tools—such as behavioral observation and IADL assessments—into planning processes can help cities tailor interventions to local aging needs. As aging accelerates across Asia, creating flexible evidence-based public environments will be vital to supporting autonomy and well-being in older populations.

5.5. Limitations

This study has several limitations. First, the cross-sectional design limits the ability to draw causal conclusions. Longitudinal studies would provide more insights into whether specific interventions, such as increasing green visibility or adding resting facilities, lead to sustained improvements in IADL performance over time. Second, while non-participant observations captured a broad overview of behavioral patterns, self-reported data on the frequency and duration of specific activities could offer a more nuanced view of how these behaviors evolve. Third, although the observation period aligns with short-term protocols [61,62,63], its limited duration may not fully capture seasonal or event-based behavioral variations. Additionally, the five-minute observation threshold may not be equally appropriate across all activity types. For example, transient behaviors like jogging or brisk walking may last less than five minutes in compact spaces, potentially leading to the underestimation or misrepresentation of such activities in the dataset. This limitation suggests the need for future research to adapt temporal thresholds based on behavior-specific and spatial contexts. Furthermore, while this study groups all observed interpersonal actions under the “social” category, it is recognized that such activities differ in function, frequency, and depth of interaction. Future research should adopt a more stratified and theory-driven framework to distinguish between casual encounters, organized group actions, and emotionally meaningful exchanges, thereby improving analytical precision and policy relevance. Finally, this study did not control for potential confounding factors such as individual health status, cognitive ability, or socioeconomic background, which may also influence IADL performance and enhance the model’s explanatory power.

These limitations underscore the need for more comprehensive study designs that account for temporal variability and a broader range of influencing factors. Expanding the scope of data collection, incorporating longitudinal approaches, and leveraging advanced analytical techniques could further refine and strengthen the findings of this study.

6. Conclusions

This study explored how physical features and behaviors in Macau’s public spaces are associated with older adults’ IADL performance. By combining spatial measurements, non-participant observations, and IADL assessments, the findings provide valuable insights into the role of public space design in supporting older adults. Even though the model explains only a small portion of the variation in IADL performance, the evidence from this study underscores the practical importance of physical space features and behavioral patterns in older adults’ functional engagement. The main findings and contributions are summarized as follows:

(1)

Associations Between Physical Features and Behavioral Patterns: The R² value of 0.125 from the regression model suggests that physical features and behavioral patterns together account for approximately 10.9% of the variance in IADL scores. This suggests that while other factors, such as personal health and chronic conditions, play a critical role in determining IADL performance, the physical features and behavioral patterns of public spaces may also relate to IADLs. Although the current model explains only a limited portion of IADL variance, it aligns with effect sizes found in similar exploratory studies and provides foundational insights for evidence-based design of age-friendly public environment;

(2)

Greenery Visibility: A higher visible greenery ratio was positively associated with IADL scores. This finding suggests that exposure to green spaces may contribute to older adults’ functional engagement, underlining the potential role of natural elements in promoting cognitive health and well-being;

(3)

Resting Facilities: The density of resting facilities, such as benches and shaded seating areas, showed a positive association with IADL scores. Spaces with abundant resting opportunities may facilitate prolonged physical activity and social interactions, which are important for maintaining independence;

(4)

Sedentary Activities: Although playing board or card games can encourage social interaction, their frequent engagement was negatively associated with IADL scores. This highlights the need to further explore the relationship between sedentary behaviors and functional independence, particularly in the context of public space usage;

(5)

Bridging Urban Design and Gerontological Health: By integrating on-site spatial measurements, non-participant observation, and a standardized IADL assessment, this study contributes to the intersection of urban design and health research. This mixed-method approach offers an exploratory perspective on how environmental factors may relate to older adults’ functional well-being;

(6)

Empirical Insights for Age-Friendly Design: This study provides preliminary evidence that physical elements may play an important role in older adults’ daily functioning. These findings offer insights for policymakers and urban designers aiming to create environments that support aging in place, particularly in dense urban contexts like Macau.

In summary, this study suggests that strategic public space design and behavioral engagement may be linked to IADL performance in older adults. It underscores the need for a holistic approach in urban planning, integrating spatial design with an understanding of individual health conditions and activity patterns. By combining gerontological insights with urban planning principles, this study contributes to the discourse on creating inclusive health-supportive environments in rapidly aging cities. Future studies could build upon these findings by incorporating longitudinal data, AI-driven behavioral analysis, and cross-cultural comparisons to further refine strategies for designing age-friendly urban spaces that enhance the independence and well-being of older adults.