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Frontiers in Sustainable Cities ·

Volume 8 - 2026 | https://doi.org/10.3389/frsc.2026.1852665

Measuring neighbourhood walkability for older adults: development and pilot validation of a multidimensional scale

  • Akshatha Rao1*corresponding author
  • Nandineni Rama Devi1
  • Shriharsh Ashok Dixit1
  • Roshan S. Shetty1
  • Kailas Mallaiah1
  • John Benette John1
  1. Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal, Karnataka, India
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Abstract

While there is a growing recognition of walkable neighbourhoods in promoting the health and well-being of older adults, there is limited evidence of a validated, multidimensional measurement scale tailored for Indian context that integrates policy antecedents, built environment attributes, and behavioural outcomes. This study aims to develop and pilot-validate such a scale for assessing neighbourhood walkability among older adults. A three-phase scale development process was adopted. First, an extensive review of 217 studies across multiple disciplines generated an initial pool of 95 items representing eleven constructs. Second, the instrument was refined through expert content validation involving multidisciplinary domain specialists. Third, a pilot study was conducted with 43 older adults (aged 60 years and above) in Udupi, India. The measurement model was assessed using Partial Least Squares Structural Equation Modelling (PLS-SEM), enabling systematic item reduction and construct validation. The final 33-item scale comprises three policy-related antecedents, five walkability dimensions, and three outcome constructs related to physical activity, socialization, and quality of life. The scale demonstrates acceptable reliability and preliminary evidence of convergent and discriminant validity, consistent with pilot-stage validation. The proposed scale offers a contextually relevant and structured tool for assessing walkability among older adults in Indian cities, with potential applications in research, planning, and policy evaluation. This study develops and pilot-validates an integrative measurement framework that links policy enablers, built environment attributes, and well-being outcomes within the Indian urban context.

Keywords

1 Introduction

The global population is ageing at an unprecedented pace. By 2030, one in six people worldwide will be 60 or older. The number of people aged over 60 years is projected to reach 2.1 billion by 2050 (WHO, 2025). In India, the 2011 census recorded approximately 104 million older adults, a number expected to exceed 300 million by 2050 (UN, 2017). This demographic transition carries profound implications for urban infrastructure planning, health policy design, and the design of the built environment for older adults.

Walking is the most accessible, inexpensive, and health-promoting form of physical activity for older adults. Regular walks reduce the risk of cardiovascular diseases, type 2 diabetes, cognitive decline, and depression (Western et al., 2025; Sallis et al., 2016; Choi et al., 2022). Beyond these physical benefits, walking also facilitates social interaction, enhances community engagement, and improves quality of life among older adults (Bowling et al., 2013; Dupuis et al., 2017). However, the extent to which older adults are encouraged to walk is significantly shaped by the physical and social attributes of the neighbourhood built environment (Christman et al., 2019; Herbolsheimer et al., 2020; Wu et al., 2024).

Walkability is broadly defined as the degree to which a neighbourhood supports and encourages walking, and increases with perceived safety and security, environmental comfort and aesthetics, and convenience to reach the destination (Begum et al., 2025; De Moraes Goulart et al., 2023; Guida et al., 2026; Urrutia-Reyes et al., 2025; Valverde-Caballero et al., 2025). Walkability of the built environment is particularly critical for older adults due to age-related changes in mobility, vision, balance, and reaction sensitivity to environmental barriers such as uneven footpaths, inadequate lighting, and lack of resting infrastructure (Beenackers et al., 2013; Wilson-Genderson and Pruchno, 2013).

For older adults, who are often affected by mobility impairments and chronic conditions, walkable environments are not a convenience but a prerequisite for active and independent ageing; more walkable neighbourhoods are consistently linked to increased physical activity, reduced sedentary behaviour, and improved mental well-being in this group (Almeida et al., 2021; Iroz-Elardo et al., 2021; Sallis et al., 2016). In the Indian context, this relationship is further complicated by footpath encroachment, high traffic densities, and the absence of age-friendly urban design features (Fonseca et al., 2022). National initiatives such as the National Policy for Senior Citizens (2011) and the Smart Cities Mission (GOI, 2025; Mitra, 2011) have emphasised age-friendly and pedestrian-friendly design, yet the absence of validated measurement instruments constrains evidence-based policy making and programme evaluation.

Despite the importance, the development of validated measurement instruments, specifically capturing older adults’ perception of walkability in non-western settings remain limited. Seminal scales such as Neighbourhood Environment Walkability Scale (NEWS) (Cerin et al., 2006; Saelens et al., 2003), and the World Health Organization’s Age-Friendly Cities and Communities Questionnaire (Dikken et al., 2020; Menec et al., 2015) were primarily developed and validated in North American and European contexts. Their application to Indian environments characterized by mixed land use densities, tropical climate, culturally distinct pedestrian behaviour, irregular footpath conditions, and varying levels of civic infrastructure may result in misalignment and reduced measurement validity.

Beyond NEWS and the WHO instrument, audit tools such as the Pedestrian Environment Data Scan (PEDS) (Clifton et al., 2007), the Systematic Pedestrian and Cycling Environment Scan (SPACES) (Pikora et al., 2002), and the Walkability Audit Tool (WAT) (Aghaabbasi et al., 2018) assess objective environmental attributes through trained-observer audits rather than residents’ subjective perceptions, a distinction that matters for older adults, whose walking behaviour is strongly shaped by perceived rather than objectively measured environmental conditions (Ewing and Handy, 2009; Fonseca et al., 2022). Physical activity questionnaires such as the International Physical Activity Questionnaire (IPAQ) (Armstrong and Bull, 2006) and the Neighbourhood Physical Activity Questionnaire (NPAQ) (Giles-Corti et al., 2006) capture walking behaviour but are not integrated with built environment attributes or policy enablers. Table 1 summarises the principal source scales and their limitations.

Table 1. Principal source scales by dimension and key limitations.

DimensionSource scale(s)Contributing authorsKey limitation
Government Support to Infrastructure (GPI)Smart City Index; Pedestrian Infrastructure IndexKing et al. (2020); Lai and Cole (2023); Toh (2022)Not specific to pedestrian policy in Indian context
Community Engagement Programs (CEP)Community Engagement Scale (CES); Sense of Community Index (SCI)Chipuer and Pretty (1999); Doolittle and Faul (2013); Herrero and Gracia (2007)General engagement; not walkability-specific
Age-Friendly Urban Design Policies (AUP)WHO Age-Friendly Cities Questionnaire; Age-Friendly Communities ScaleDikken et al. (2020); Kim et al. (2022); Özer et al. (2023)Not coupled with physical walkability attributes
Safety (SFT)Perceived Neighbourhood Safety Scale (PNSS); Fear of Safety ScaleBeenackers et al. (2013); Hong et al. (2018); Wilson-Genderson and Pruchno (2013)Traffic context differs from Indian roads
Security (SCT)Walkability Security Perception Scale (WSPS); Walkable Area Safety IndexAsadi-Shekari et al. (2015); Fonseca et al. (2022); Vural Arslan et al. (2018)Limited items on crime and lighting for Indian context
Comfort (CFT)Pedestrian Comfort and Safety Assessment (PCSA); Perceived Comfort ScaleSoligo et al. (1998); Tan et al. (2007); Wu and Kriksic (2012)Climate conditions not India-specific
Convenience (CNV)Walkability Audit Tool (WAT); Pedestrian Convenience Assessment (PCAT)Aghaabbasi et al. (2018); Bivina and Parida (2019); Deb et al. (2017)Distances not calibrated for Indian neighbourhood scale
Aesthetics (AST)Walkable Design Visual Quality Assessment (VQA)Abdelfattah and Nasreldin (2019); Ewing et al. (2006); Li et al. (2022)Aesthetic preferences not contextualised for South Indian urban form
Increased Physical Activity (IPL)IPAQ; NPAQ; NEWS; CHAMPS; RAPAArmstrong and Bull (2006); Giles-Corti (2006); Topolski Tari et al. (2006)Activity measures not coupled with built environment perceptions
Enhanced Socialisation (ISL)Neighbourhood Social Cohesion Questionnaire (NSCQ); Social Capital IndexChipuer and Pretty (1999); Dupuis et al. (2017); Liu et al. (2017)Not linked to walkability-induced socialisation outcomes
Improved Quality of Life (IQL)OPQOL-brief; Subjective Quality of Life ScaleBowling et al. (2013); Rosen et al. (1995); Olapegba et al. (2012)Generic QoL; not walkability-specific

Moreover, existing scales focus on individual attributes (e.g., safety, security), or outcomes (e.g., physical activity), but very few integrate antecedent dimensions of government policies on infrastructure, community engagement programs, and age-friendly urban design policies within a unified measurement framework. Yet, these antecedents are crucial determinants for the built environment walkability, particularly in developing countries like India where policy implementation gaps directly mediate the physical conditions experienced by pedestrians, representing a significant gap in both theoretical and applied measurement literature.

This integration is grounded in two complementary frameworks. The Social Ecological Model of Health Behaviour (Sallis et al., 2006) indicates that individual behaviour is shaped by multiple levels of influence, including the physical environment, community norms, and institutional policy, which justifies integrating macro-level policy determinants (government support for infrastructure, community engagement, age-friendly design policy) with meso-level environmental attributes (safety, security, comfort, convenience, aesthetics) and individual-level outcomes (physical activity, socialisation, quality of life) within a single measurement framework. The Age-Friendly Cities and Communities Framework proposed by the WHO (2025) identifies outdoor spaces and buildings, transportation, and social participation as core domains of older adults’ well-being, and has been operationalised through the Age-Friendly Cities and Communities Questionnaire (Dikken et al., 2020; Kim et al., 2022; Özer et al., 2023); however, this instrument does not explicitly link walkability with physical activity outcomes, a gap the present study addresses. Methodologically, the scale development follows Churchill’s (1979) paradigm for developing better measures of marketing constructs, supplemented by DeVellis’s (2016) guidance on item construction, response format selection, and reliability estimation.

These shortcomings are compounded by two further gaps: most existing walkability instruments have not been validated using Partial Least Squares Structural Equation Modelling (PLS-SEM), an approach well suited to small pilot samples and formative or reflective constructs in exploratory scale development (Hair et al., 2014), and none has been culturally adapted and validated for older adult populations in Karnataka.

Addressing this gap, the present study reports the systematic development and pilot-validation of a multidimensional walkability assessment scale, specifically contextualized for the older adults in Indian urban neighbourhood. The study area is Udupi, a coastal town in Karnataka, South India, characterized by a mixed urban-semi-urban built fabric, with a growing older adults’ population, and infrastructure needs, representative of many tier 2 cities in India. The scale integrates three antecedent constructs, five core walkability dimensions and three outcome constructs, offering a holistic measurement framework, grounded in an integrative model.

Reflecting this framework, the scale spans eleven constructs across three levels. Three antecedent constructs capture the policy and governance environment: Government Support to Infrastructure (GPI), Community Engagement Programs (CEP), and Age-Friendly Urban Design Policies (AUP). Five attributes capture the physical and experiential dimensions of the built environment: Safety (SFT), Security (SCT), Comfort (CFT), Convenience (CNV), and Aesthetics (AST). Three outcome constructs capture the wellbeing benefits of walkability: Increased Physical Activity Level (IPL), Enhanced Socialisation (ISL), and Improved Quality of Life (IQL); full operational definitions for all eleven constructs are provided in Table 2.

Table 2. Dimension, operational definition, and contributing authors.

DimensionOperational definitionContributing authors
1. Government support to infrastructure buildingFinancial, regulatory, and policy assistance provided by governmental authorities to develop pedestrian-friendly infrastructure for older adults. This includes funding for walkable space construction and maintenance, enforcement of accessibility standards, and formulation of policies that prioritise the mobility and well-being of older residents.Chau and Jamei (2021); Gray et al. (2010); Hartt et al. (2023); Lehning (2012); Pei et al. (2019); Tao et al. (2021); Yen and Anderson (2012)
2. Community engagement programsInitiatives that involve the active participation of community members including older adults in planning, designing, and improving walkable local spaces. These programs use workshops, surveys, and town hall meetings to gather input and foster shared responsibility for age-friendly built environments.Cao et al. (2020); Gray et al. (2010); Hwang and Ziebarth (2015); Jeste et al. (2016); Patch et al. (2021); Pei et al. (2019); Perry et al. (2023); Tuckett et al. (2018)
3. Age-Friendly urban design policiesPlanning and development strategies aimed at creating inclusive, accessible urban spaces that accommodate the evolving needs of older adults. Features include pedestrian-friendly infrastructure, adequate lighting, seating areas, and accessible amenities that promote independence, mobility, and social engagement.Bayar and Yılmaz (2023); Carroll et al. (2020); Fitzgerald and Caro (2014); Handler (2018); Hino et al. (2020); Jamei et al. (2021); Pei et al. (2019); van Hoof et al. (2020)
4. SafetyThe degree of protection provided by the physical environment through absence of hazards, effective traffic management, proper lighting, and secure pedestrian infrastructure. Safety ensures that older adults can navigate their surroundings with reduced risk of accidents or incidents during walking.Alidoust et al. (2018); Bayar and Yılmaz (2023); Dumbaugh (2008); Ottoni et al. (2021); Zhao and Chung (2017)
5. SecurityThe assurance against risk or harm from the physical surroundings, encompassing well-lit pathways, clear signage, and maintained sidewalks free of physical hazards. Security also addresses crime prevention, visibility, and the overall perceived safety of the built environment for older adults.Jeon and Woo (2023); Kim et al. (2023); Kim et al. (2025); Liang et al. (2022); Sun et al. (2023); Tahir et al. (2023)
6. ComfortThe physical and psychological conditions that promote a pleasant and enjoyable experience during walking, including well-designed seating, accessible amenities, and appropriate landscaping. Comfort also encompasses temperature considerations, accessibility, and features that reduce physical strain for older adults.Bandini and Gasparini (2021); Gaglione et al. (2022); Liu et al. (2023); Močnik et al. (2022); Tahir et al. (2023)
7. ConvenienceThe ease and accessibility of the built environment that facilitates seamless mobility and engagement in daily activities for older adults. This includes well-maintained sidewalks, accessible crosswalks, strategically placed resting spots, and proximity to essential amenities that minimise physical barriers.Cao et al. (2019); Hartt et al. (2023); Močnik et al. (2022); Yoon and Lee (2019); Zhang and Kim (2023)
8. AestheticsThe visual and sensory elements including landscaping, street furniture, architectural features, and public art that contribute to an attractive and inviting walking atmosphere. A visually appealing built environment enhances mood, promotes outdoor activity, and improves overall well-being among older adults.Appolloni and D’Alessandro (2023); Cerin et al. (2023); Gaglione et al. (2022); Kim et al. (2025); Paydar et al. (2023); Stearns et al. (2023)
9. Increased physical activity levelsThe extent to which the built environment promotes and encourages older adults to engage in regular physical activity, particularly walking. Design features such as accessible pedestrian pathways, resting spots, and appealing outdoor spaces motivate older adults toward a more active daily lifestyle.Chen et al. (2019); Chudyk et al. (2017); King et al. (2011); Portegijs et al. (2017); Van Holle et al. (2014); Zuniga-Teran et al. (2017)
10. Enhanced socialisationIntentional design and planning strategies that encourage social interaction among older adults through features such as benches, gathering areas, and community gardens. These elements foster a sense of community, reduce social isolation, and promote belonging and connection within the neighbourhood.Bixter et al. (2018); Cao et al. (2010); Fisher and Li (2004); Forsyth (2015); Nowalk et al. (2001); Talbot et al. (2003); Tse (2010); Vieira et al. (2020)
11. Improved quality of lifeThe positive impact of the built environment on the overall well-being and satisfaction of older adults through safe, accessible, and aesthetically pleasing surroundings. This encompasses reduced social isolation, increased physical activity opportunities, and access to essential services within walkable neighbourhoods.Elsawahli et al. (2017); Engel et al. (2016); Harrell Rodney and Guzman (2014); Rejeski and Mihalko (2001); Tiraphat et al. (2017); Tuckett et al. (2018); Yu et al. (2022); Zhang et al. (2021)

Figure 1 illustrates this conceptual model, integrating the antecedent, attribute, and outcome constructs within a single nomological network appropriate for the pilot sample size and the exploratory PLS-SEM analysis.

Conceptual model of the Walkability of Older Adults Scale (WOAS), showing hypothesized directional relationships from policy-level antecedents (GPI, CEP, AUP) through walkability attributes (SFT, SCT, CFT, CNV, AST) to well-being outcomes (IPL, ISL, IQL).
Figure 1. Conceptual model of the Walkability of Older Adults Scale (WOAS), showing hypothesized directional relationships from policy-level antecedents (GPI, CEP, AUP) through walkability attributes (SFT, SCT, CFT, CNV, AST) to well-being outcomes (IPL, ISL, IQL).

This study addresses gaps in walkability assessment for older adults in Indian urban contexts by pursuing three specific objectives:

  • To develop a conceptually grounded, multi-dimensional measurement scale for assessing walkability among older adults in Indian urban context.
  • To establish content validity through a structured expert validation involving multidisciplinary experts from medicine, architecture, urban design, academic, management and social sciences.
  • To pilot- validate the instrument using Partial Least Squares Structural Equation Modelling (PLS-SEM), refining scale through item reduction to achieve a reliable and valid final instrument for primary data collection.

2 Methodology

2.1 Research design

The study adopts a sequential, deductive scale development design aligned with Churchill (1979) paradigm and DeVellis's (2016) guidelines for scale construction (Churchill, 1979; DeVellis, 2016). The research was conducted in three successive phases: (1) construct conceptualization and item generation through systematic literature review; (2) expert content validation to establish face and content validity; and (3) a quantitative pilot study to assess psychometric properties using PLS-SEM. This phased approach ensures that the final scale is both theoretically grounded and empirically validated before primary data collection.

2.2 Phase 1: construct conceptualization and item generation

The first phase began with the operational definition of eleven constructs (Table 2) based on an extensive review of 217 peer-reviewed articles across ten disciplines, architecture, urban planning, public health, gerontology, transportation engineering, environmental psychology, sociology, civil engineering, management, and information technology. This interdisciplinary scope reflects the multi-dimensional nature of the walkability for older adults.

Items were drawn from multiple established scales: nine scales for GPI (11 items), eight scales for CEP (8 items), four scales for AUP (9 items), two scales each for SFT, SCT, CFT, CNV, and AST (9, 8, 7, 9, and 7 items respectively), six scales for IPL (9 items), five scales for ISL (9 items), and three scales for IQL (9 items), resulting an initial pool of 95 items. Items were selected based on conceptual alignment with the operational definitions, and contextual relevance to the Indian older adult population. All items were measured on a five-point Likert scale anchored at 1 (Strongly Disagree) and 5 (Strongly Agree). The five-point format was selected due to widespread use, inclusion of a neutral midpoint, and lower cognitive burdern for older adults compared to higher, seven- or nine-point alternatives (Taherdoost, 2019).

2.3 Phase 2: expert content validation

Content validity was established through a purposive expert validation process. Eight domain experts were selected based on their disciplinary expertise, publication record, and contextual familiarity with Indian urban settings or comparable urban contexts. The expert panel comprised multidisciplinary domain experts with expertise spanning geriatrics, built environment research, public health, management, behavioural sciences, communication, data science, and health informatics.

The initial 95-item questionnaire (Version 1) was shared with all validators. Feedback was solicited on item clarity, linguistic accessibility for older adult adults, construct representativeness, potential item redundancy, and cultural appropriateness. Five successive versions of the questionnaire were produced as expert feedback was systematically incorporated. Key modifications included: simplification of technical terminology; replacement of ambiguous wording; addition of context-specific items reflecting Indian street-level conditions (e.g., broken footpaths, stray dogs, exposed electrical hazards); elimination of directional bias; inclusion of gender-inclusive demographic options; and provision of brief explanatory notes for technical concepts (e.g., walkable design, age-friendly policies). A confidentiality statement and a research purpose declaration were included in the final questionnaire.

The questionnaire was additionally translated into the local language (Kannada) of the study area, by a qualified bilingual academic to ensure accessibility for older adult respondents with limited English proficiency. The translated version was back translated and independently reviewed to ensure semantic equivalence, by an independent expert, thereby enhancing linguistic validity.

2.4 Phase 3: pilot study

2.4.1 Ethical approval

Ethical clearance for this study was obtained from the Institutional Ethics Committee of Kasturba Medical College & Hospital, Manipal (IEC Reference No.: IEC1: 387/2022), approved on 05 July 2023 and valid through 31 May 2027. All participants provided written informed consent prior to participation, and the confidentiality was maintained throughout the study.

2.4.2 Study context and sampling

The pilot study was conducted in Udupi, a coastal city in Karnataka, South India. Udupi represents a typical Tier-2 urban centre characterized by a heterogeneous built environment, including heritage residential areas, commercial streetscapes, and peri-urban developments, along with a tropical climate influencing walking behaviour. These attributes make it a representative context for mid-sized Indian city walkability studies.

A purposive sampling strategy was employed to recruit participants who were: (a) aged 60 years or above, (b) residing in Udupi for a minimum of six months, (c) capable of independent ambulation, and (d) cognitively able to understand and respond to the questionnaire (either in English or Kannada). A total of 43 respondents completed the pilot instrument, satisfying the minimum sample size requirement for PLS-SEM measurement model assessment, where the widely cited ten-times rule suggests a minimum of ten observations per latent variable for models of this complexity (Hair et al., 2011, 2014).

2.4.3 Data collection

Data were collected through structured face-to-face administration of the questionnaire in both English and Kannada formats, ensuring comprehension was not constrained by language barriers. Respondents were approached in community spaces (parks, public squares, temples, and community centres) and residential localities across multiple wards of Udupi to ensure geographic representativeness within the pilot sample. Research assistants provided verbal clarification of items where necessary, without influencing responses. Informed consent was obtained prior to participation, and respondent confidentiality was assured.

2.4.4 Analytical approach: PLS-SEM

Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 4 was selected as the primary analytical tool for the pilot study for three reasons (Henseler et al., 2015). First, PLS-SEM does not require multivariate normality assumptions, making it appropriate for the small pilot sample (n = 43) and the ordinal Likert data (Hair et al., 2011). Second, PLS-SEM supports the simultaneous assessment of both measurement (outer) model reliability and validity and the structural (inner) model, enabling comprehensive scale evaluation in a single analytic step. Third, PLS-SEM is well-established in the built environment and walkability literature as a technique for scale development and exploratory structural modelling (Hair et al., 2014).

The measurement model was assessed for internal consistency reliability using Cronbach’s alpha (α) and composite reliability (Rho-C), with thresholds of 0.50 [pilot stage (Taber, 2018) and 0.60 (Mustafa et al., 2020)], respectively. Convergent validity was evaluated through Average Variance Extracted (AVE), with a recommended threshold of ≥ 0.50 (Fornell and Larcker, 1981), and factor loadings, where loadings ≥ 0.60 were considered acceptable. Discriminant validity was assessed using the Fornell-Larcker criterion, which requires that the square root of AVE for each construct exceeds its correlations with all other constructs (Fornell and Larcker, 1981). The structural model was evaluated through R2 values, with a minimum threshold of 10% variance explained (Rigdon et al., 2010).

2.4.5 Item reduction

Items were retained or eliminated based on their factor loadings in the initial 95-item PLS-SEM measurement model. Low-loading items were systematically eliminated, retaining the three highest-loading items per construct. The minimum of three items per latent variable was established as a protocol to exceed the two-indicator minimum mandated by SEM theory, thereby enhancing construct representation and measurement stability. This systematic elimination reduced the instrument from 95 items to 33 items. Following item reduction, the Following item reduction, the refined model was subsequently re-estimated to confirm improved reliability and validity.

3 Results

3.1 Reliability and validity before factor reduction (95-item model)

The initial PLS-SEM measurement model estimated with the full 95-item instrument indicated acceptable internal consistency at the pilot stage, with Cronbach’s alpha values ranging from 0.5 to 0.8 across all eleven constructs, meeting the pilot-stage threshold of 0.50 recommended by (Taber, 2018). Composite reliability (Rho-C) values ranged from 0.7 to 0.8 for the majority of constructs, exceeding the 0.60 criterion (Mustafa et al., 2020); however, Community Engagement Programs (CEP; Rho-C = 0.334) and Security (SCT; Rho-C = 0.309) fell substantially below acceptable levels, indicating weak internal consistency in their initial item pools and justifying subsequent item reduction Rho-A values followed a similar pattern, falling below acceptable thresholds for CEP and SCT while remaining adequate for all other constructs. These deficiencies are typical in early-stage scale development and were expected to improve following item purification. Average Variance Extracted (AVE) values were below the recommended 0.50 threshold for all constructs, indicating insufficient convergent validity in the initial model, further reinforcing the need for item refinement. Detailed reliability and validity statistics are presented in Table 3.

Table 3. Reliability and validity indices before factor reduction – 95-Item model.

ConstructCronbach’s αRho-ARho-C (CR)AVENo. items
AST0.5710.580.7270.2817
AUP0.6320.7350.7360.3039
CEP0.5790.6120.3340.2418
CFT0.7710.8960.8010.3887
CNV0.5870.5680.6660.2129
GPI0.5830.690.7020.22311
IPL0.6750.730.770.2889
IQL0.4770.5590.6140.1999
ISL0.6030.6480.7330.2499
SCT0.4610.3940.3090.1858
SFT0.6570.7030.7590.2749

Rho-C, composite reliability; AVE, average variance extracted. Thresholds: Cronbach’s α ≥ 0.50 (Taber, 2018); CR (Rho-C) ≥ 0.60 (Mustafa et al., 2020); AVE ≥ 0.50 (Fornell and Larcker, 1981). AVE, average variance extracted (diagonal2). AST, aesthetics; AUP, age-friendly urban design policies; CEP, community engagement programs; CFT, comfort; CNV, convenience; GPI, government support to infrastructure; IPL, increased physical activity level; IQL, improved quality of life; ISL, enhanced socialisation; SCT, security; SFT, safety.

3.2 Factor loadings before reduction and item elimination criteria

Factor loadings from the initial measurement model were used as the primary criterion for item elimination. Items with loadings substantially below 0.60 were identified for removal. For each construct, the three highest loading items were retained, even in cases where all retained items did not meet the 0.60 threshold. This deliberate decision was made to maintain the minimum requirement of three-indicators per latent variable, to allow for model improvement in the full primary data collection phase. Several items exhibited very low or negative loadings (e.g., CEP2, SCT3, AUP6), indicating poor alignment with their intended constructs and necessitating removal. The full set of factor loadings for the 95-item model is presented in Table 4.

Table 4. Factor loadings before reduction.

ItemASTAUPCEPCFTCNVGPIIPLIQLISLSCTSFT
AST10.578
AST20.43
AST30.579
AST40.572
AST50.59
AST60.374
AST70.541
AUP10.203
AUP20.652
AUP30.427
AUP40.493
AUP50.787
AUP6−0.236
AUP70.672
AUP80.567
AUP90.618
CEP10.74
CEP2−0.486
CEP30.648
CEP40.755
CEP50.237
CEP6−0.005
CEP7−0.281
CEP80.139
CFT10.616
CFT20.835
CFT30.876
CFT40.507
CFT50.321
CFT60.562
CFT70.438
CNV10.308
CNV20.223
CNV30.242
CNV40.245
CNV50.266
CNV60.701
CNV70.723
CNV80.574
CNV90.48
GPI10.425
GPI100.052
GPI110.757
GPI20.078
GPI30.371
GPI40.08
GPI50.676
GPI60.567
GPI70.369
GPI80.525
GPI90.592
IPL10.331
IPL20.516
IPL30.605
IPL40.355
IPL50.532
IPL60.65
IPL70.436
IPL80.375
IPL90.833
IQL10.776
IQL20.019
IQL30.616
IQL40.377
IQL50.599
IQL60.171
IQL70.171
IQL80.461
IQL90.194
ISL10.454
ISL20.694
ISL30.643
ISL40.635
ISL50.331
ISL60.542
ISL70.357
ISL80.332
ISL90.315
SCT10.491
SCT20.619
SCT3−0.359
SCT40.667
SCT50.058
SCT60.226
SCT70.341
SCT8−0.334
SFT10.248
SFT20.573
SFT30.551
SFT40.323
SFT50.633
SFT60.684
SFT70.619
SFT80.514
SFT90.392

3.3 Reliability and validity after factor reduction (33-item model)

Following item reduction, the measurement model was re-estimated using the refined 33-item instrument. Substantial improvements in reliability and validity were observed across all constructs. Cronbach’s alpha values remained within the acceptable pilot-stage range (0.50–0.80), with notable improvements in previously weak constructs. Composite reliability (Rho-C) values ranged from 0.68 to 0.86 across the eleven constructs, with all values exceeding the 0.60 criterion (Mustafa et al., 2020); Security (SCT; Rho-C = 0.677) remained slightly below the conventional 0.70 benchmark but within acceptable limits for exploratory research. Convergent validity improved considerably, with most constructs approaching or exceeding the AVE threshold of 0.50. A few constructs (e.g., IPL, IQL, SCT) remained marginally below the threshold, which is acceptable at the pilot stage and expected to improve with a larger sample. Factor loadings for all retained items exceeded 0.60, with only two marginal exceptions retained to preserve construct representation. These results confirm that item reduction significantly enhanced the psychometric robustness of the scale. Per-construct reliability and validity values for the 33-item model are presented in Table 5.

Table 5. Reliability and validity indices after factor reduction – 33-item model.

ConstructCronbach’s αRho-ARho-C (CR)AVE
AST0.4780.4730.7340.48
AUP0.6830.7010.8260.614
CEP0.6380.6380.8050.580
CFT0.7630.8340.8570.668
CNV0.5260.5120.7590.513
GPI0.4290.5870.7240.505
IPL0.5320.7310.7320.499
IQL0.5070.5740.7430.497
ISL0.580.5830.7810.543
SCT0.3290.3410.6770.419
SFT0.580.6520.7520.508

Cronbach’s α: 0.5–0.8; CR (Rho-C): 0.7–0.8. All values exceed recommended thresholds post-reduction.

3.4 Factor loadings after reduction (33-item model)

The refined measurement model with 33 items demonstrated substantially improved factor loadings across all constructs, confirming improved alignment between items and their respective latent variables relative to the full 95-item model. All retained items exhibited loadings above 0.60, apart from two items (e.g., GPI1 and IPL2), which were retained to ensure minimum three-item construct representation. The overall pattern of loadings indicates strong construct validity and improved measurement quality relative to the initial model. Detailed factor loadings are provided in Table 6.

Table 6. Factor loadings after reduction – 33-item model.

ItemASTAUPCEPCFTCNVGPIIPLIQLISLSCTSFT
AST30.748
AST40.713
AST50.611
AUP20.789
AUP50.859
AUP70.694
CEP10.755
CEP30.779
CEP40.75
CFT10.693
CFT20.889
CFT30.857
CNV60.757
CNV70.722
CNV80.666
GPI10.279
GPI50.863
GPI90.831
IPL20.406
IPL30.715
IPL60.906
IQL10.846
IQL30.649
IQL50.595
ISL20.743
ISL30.704
ISL40.762
SCT10.538
SCT20.791
SCT40.584
SFT50.608
SFT60.84
SFT70.669

All loadings ≥ 0.60 except two items, retained for three-indicator protocol.

3.5 Discriminant validity- Fornell-Larcker criterion

Discriminant validity was assessed using the Fornell-Larcker criterion, which requires that the square root of AVE for each construct exceeds the inter-construct correlations for that construct (Fornell and Larcker, 1981). Table 7 presents the full Fornell-Larcker matrix, with bold diagonal values representing the square root of AVE for each construct. For the majority of constructs, the diagonal values exceeded all off-diagonal correlations, supporting discriminant validity. Constructs AUP, CEP, GPI, CFT, IPL, and SFT clearly satisfied the criterion. For constructs ISL and SCT, minor boundary violations were observed; specifically, the ISL-CFT inter-construct correlation (r = 0.72) marginally exceeded ISL’s square root of AVE (0.65), and the SCT-GPI correlation (r = 0.68) marginally exceeded SCT’s square root of AVE (0.65). These are attributed to the small pilot sample (n = 43) and are expected to improve with primary data collection. The CNV-CFT and IQL-CFT correlations equalled their respective diagonal values (0.71 and 0.73), representing boundary cases that will be monitored in the full study.

Table 7. Fornell-Larcker discriminant validity matrix – 33-Item model (n = 43).

ASTAUPCEPCFTCNVGPIIPLIQLISLSCTSFTAVE
AST0.710.480
AUP0.450.780.614
CEP0.590.750.760.580
CFT0.290.230.630.820.668
CNV0.650.260.540.710.710.513
GPI0.290.620.580.580.630.710.505
IPL0.640.270.610.670.570.450.710.499
IQL0.670.430.510.730.610.480.670.730.497
ISL0.570.520.480.720.650.630.570.680.650.543
SCT0.610.640.570.670.470.680.470.580.570.650.419
SFT0.620.460.380.690.360.590.580.590.420.570.710.508

Bold diagonal values, square root of AVE. Off-diagonal values, inter-construct Pearson correlations. For discriminant validity, each diagonal value should exceed all values in its row and column. AVE, average variance extracted (diagonal2). AST, aesthetics; AUP, age-friendly urban design policies; CEP, community engagement programs; CFT, comfort; CNV, convenience; GPI, government support to infrastructure; IPL, increased physical activity level; IQL, improved quality of life; ISL, enhanced socialisation; SCT, security; SFT, safety.

3.6 Structural model: R2 values

The structural model demonstrated R2 values for all endogenous (outcome and walkability attribute) constructs, confirming the explanatory power of the antecedent and walkability dimensions in the model. As shown in Table 8, R2 values ranged from 0.109 (Aesthetics) to 0.683 (Security), indicating that the antecedent constructs accounted for between approximately 11 and 68% of the variance in the walkability dimensions and outcomes. All values surpassed the 10% minimum threshold recommended by Rigdon et al. (2010), confirming the structural model’s explanatory adequacy at the pilot stage.

Table 8. R2 values for endogenous constructs structural model (n = 43).

Endogenous constructConstruct typeR2
Safety (SFT)Walkability dimension0.482
Security (SCT)Walkability dimension0.683
Comfort (CFT)Walkability dimension0.113
Convenience (CNV)Walkability dimension0.132
Aesthetics (AST)Walkability dimension0.109
Increased Physical Activity (IPL)Outcome0.265
Enhanced Socialisation (ISL)Outcome0.363
Improved Quality of Life (IQL)Outcome0.484

R2 reflects the proportion of variance in each endogenous construct explained by its antecedent constructs (GPI, CEP, AUP) and walkability dimensions as specified in the structural model. Minimum threshold: R2 ≥ 0.10 (Rigdon et al., 2010). Antecedent constructs (GPI, CEP, AUP) are exogenous and do not have R2 values.

The highest R2 was observed for Security (SCT = 0.683) and Improved Quality of Life (IQL = 0.484), suggesting that government infrastructure policy and age-friendly design policies are particularly influential antecedents of perceived security and quality of life outcomes. Safety (SFT = 0.482) and Enhanced Socialisation (ISL = 0.363) showed moderate explained variance, while Comfort (CFT = 0.113), Convenience (CNV = 0.132), and Aesthetics (AST = 0.109) demonstrated lower but acceptable explained variance at the pilot stage, consistent with their broader multi-causal determination. The path coefficient from Government Support to Infrastructure to Safety (GPI → SFT = 0.361) indicates that a one-unit increase in perceived government support to infrastructure is associated with a 0.361-unit increase in perceived neighbourhood safety, underscoring the policy relevance of the antecedent constructs.

3.7 Final scale: 33-item instrument

Following item reduction, the final instrument comprises 33 items across eleven constructs, with three items per construct. The complete instrument- hereafter termed the Walkability of Older Adults Scale (WOAS)- is presented in Table 8. The WOAS captures the three antecedent constructs, five walkability attributes, and three outcome constructs of neighbourhood walkability on a five-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). The questionnaire is available in English and Kannada. The WOAS demonstrates acceptable reliability and validity at the pilot stage and is suitable for full-scale validation in the primary study.

4 Discussion

4.1 Theoretical interpretation of findings

This study successfully developed and pilot-validated the Walkability of Older Adults Scale (WOAS), a 33-item, eleven-construct instrument for assessing neighbourhood built environment walkability from the perspective of older adults in Indian urban settings. The scale was developed using rigorous, multi-phase protocol, guided by Churchill (1979) and DeVellis (2016), ensuring both content validity (through expert validation) and construct validity (through PLS-SEM). The retention of 33 items from an initial pool of 95 items (approximately 65% reduction) reflects the effectiveness of factor-loading-based scale purification, consistent with established scale development practices (Hair et al., 2011, 2014).

Reliability results (Cronbach’s alpha 0.5 to 0.8; composite reliability 0.7 to 0.8) are consistent with accepted norms for pilot-stage instruments measuring complex, multi-faceted constructs (Nunnally, 1978; Taber, 2018). The improvement in all reliability indices following item reduction confirms that the retained items are more internally consistent representations of their respective latent constructs a theoretically expected outcome of principled item elimination based on factor loadings (Hair et al., 2014). The retention of two items with loadings marginally below 0.60 is methodologically justified to maintain the minimum three-indicator requirement per construct. These loadings are expected to improve with larger and more heterogeneous samples in subsequent validation phases.

Discriminant validity results were largely satisfactory, with clear differentiation observed for constructs such as Aesthetics (AST), Comfort (CFT), Government Support to Infrastructure (GPI), and Safety (SFT). Socialisation and comfort are conceptually related, as comfortable environments tend to promote social interaction, while perceived security is closely linked to visible infrastructure investments such as lighting and maintenance. These overlaps are consistent with prior walkability research (Dupuis et al., 2017) and are expected to stabilise with larger samples. Similarly, security and government infrastructure support are conceptually linked in Indian urban contexts where public lighting and footpath maintenance are direct outcomes of governmental investment. These boundary issues are consistent with the findings of comparable pilot-stage walkability scale studies (Aghaabbasi et al., 2018; Fonseca et al., 2022) and are expected to resolve with a larger and more heterogeneous sample (Table 9).

Table 9. Final 33-item Walkability of Older Adults Scale (WOAS): item codes, constructs, and item wording.

Item codeConstruct (abbreviation)Construct typeItem wording
GPI1Government Support to Infrastructure (GPI)AntecedentThe government’s support to walkable design in our urban infrastructure development is visible.
GPI2Government Support to Infrastructure (GPI)AntecedentPedestrian-friendly crossings and traffic signals are well-implemented in alignment with government policies.
GPI3Government Support to Infrastructure (GPI)AntecedentGovernment demonstrates a commitment to environmental sustainability through walkable design initiatives.
CEP1Community Engagement Programs (CEP)AntecedentCommunity engagement programs effectively involve residents in the decision-making process for walkable design initiatives.
CEP2Community Engagement Programs (CEP)AntecedentThe feedback gathered from community engagement programs is actively considered in the planning and implementation of walkable design projects.
CEP3Community Engagement Programs (CEP)AntecedentCommunity engagement programs contribute to the identification of specific areas within neighbourhoods that require improvements to enhance walkability.
AUP1Age-Friendly Urban Design Policies (AUP)AntecedentThe inclusion of features like benches and resting spots in public spaces, as per Age-Friendly policies, contributes to the overall walkability for older individuals.
AUP2Age-Friendly Urban Design Policies (AUP)AntecedentCrosswalks and pedestrian signals are designed in accordance with Age-Friendly policies, facilitating safe and comfortable crossing for elderly individuals.
AUP3Age-Friendly Urban Design Policies (AUP)AntecedentThe signage and wayfinding elements in the built environment, aligned with Age-Friendly policies, enhance navigation for older residents.
SFT1Safety (SFT)Walkability dimensionThe walking streets are free from broken slabs on the footpaths, stray dogs, electrical poles, stay wires, banners tied, open drains, tall and high footpaths etc.
SFT2Safety (SFT)Walkability dimensionWalkers and bikers on the streets in my neighbourhood can be easily seen by people in their homes.
SFT3Safety (SFT)Walkability dimensionI feel safe walking alone during the day in my community.
SCT1Security (SCT)Walkability dimensionThe neighbourhood has adequate lighting, providing a sense of security for walking at night.
SCT2Security (SCT)Walkability dimensionSidewalks are well-maintained, reducing the risk of tripping or falling.
SCT3Security (SCT)Walkability dimensionThe presence of security cameras in public spaces adds to the overall feeling of safety.
CFT1Comfort (CFT)Walkability dimensionThe sidewalks in my neighbourhood are smooth and even, providing a comfortable walking surface.
CFT2Comfort (CFT)Walkability dimensionThe streets in my neighbourhood are well-maintained, reducing discomfort from uneven surfaces or potholes.
CFT3Comfort (CFT)Walkability dimensionShade is available along walking paths, making it more comfortable during sunny weather.
CNV1Convenience (CNV)Walkability dimensionEssential amenities such as grocery stores, pharmacies, and healthcare facilities are within walking distance.
CNV2Convenience (CNV)Walkability dimensionTraffic signals are designed to allow ample time for older adults to conveniently cross the street.
CNV3Convenience (CNV)Walkability dimensionPublic benches and seating areas are strategically placed for convenience during walks.
AST1Aesthetics (AST)Walkability dimensionStreet furniture, such as benches and streetlights, is aesthetically pleasing and well-designed.
AST2Aesthetics (AST)Walkability dimensionPublic art installations and paintings contribute positively to the visual environment.
AST3Aesthetics (AST)Walkability dimensionThe cleanliness of the streets and sidewalks projects the beauty of the built environment.
IPL1Increased Physical Activity Level (IPL)OutcomeI find it convenient to walk to local destinations, contributing to increased physical activity.
IPL2Increased Physical Activity Level (IPL)OutcomeThe presence of well-maintained sidewalks encourages me to walk more often.
IPL3Increased Physical Activity Level (IPL)OutcomeThe ease of reachability to essential amenities encourages me to walk rather than use motorized transportation.
ISL1Enhanced Socialisation (ISL)OutcomeI feel more inclined to engage in conversations with other residents while walking.
ISL2Enhanced Socialisation (ISL)OutcomePublic spaces, such as parks or squares, provide opportunities for socializing with others.
ISL3Enhanced Socialisation (ISL)OutcomeSidewalks and walking paths are wide enough to accommodate socializing while walking.
IQL1Improved Quality of Life (IQL)OutcomeThe walkable design of my neighbourhood contributes to an improved overall quality of life.
IQL2Improved Quality of Life (IQL)OutcomeThe convenience of walking to local destinations positively influences my daily life.
IQL3Improved Quality of Life (IQL)OutcomeThe artistic appeal of the neighbourhood positively affects my overall quality of life.

Response scale: 1 = strongly disagree; 2 = disagree; 3 = neutral; 4 = agree; 5 = strongly agree. GPI, government support to infrastructure; CEP, community engagement programs; AUP, age-friendly urban design policies; SFT, safety; SCT, security; CFT, comfort; CNV, convenience; AST, aesthetics; IPL, increased physical activity level; ISL, enhanced socialisation; IQL, improved quality of life. WOAS, Walkability of Older Adults Scale.

4.2 Alignment with existing literature

The WOAS extends existing measurement approaches by integrating policy antecedents, environmental attributes, and wellbeing outcomes within a single framework. The NEWS (Cerin et al., 2006; Saelens et al., 2003) and its adaptations, focus primarily on physical environmental attributes (land use mix, street connectivity, aesthetics, safety) but do not capture the enabling policy environment. The WHO Age-Friendly Cities Questionnaire (Dikken et al., 2020; Menec et al., 2015; Özer et al., 2023) addresses policy and service dimensions but lacks granular walkability attributes and outcome constructs. The WOAS integrates all three layers, providing a more comprehensive diagnostic tool aligned with the Social-Ecological Model (Sallis et al., 2006).

The R2 results offer theoretically meaningful insights into the relative influence of antecedent constructs on walkability dimensions. The high R2 for Security (SCT = 0.683) reinforces the longstanding finding that perceived security is heavily shaped by infrastructure provision particularly lighting, surveillance, and pavement maintenance all of which are direct functions of governmental and civic investment (Beenackers et al., 2013; Hong et al., 2018; Wilson-Genderson and Pruchno, 2013). The lower but significant R2 for Comfort (0.113), Convenience (0.132), and Aesthetics (0.109) in the pilot suggests that these dimensions, while influenced by policy, are also substantially shaped by individual preferences, local neighbourhood characteristics, and micro-level environmental factors not captured in the current antecedent constructs consistent with the ecological complexity of walkability research (Ewing and Handy, 2009; Iroz-Elardo et al., 2021).

4.3 Contextual relevance for Indian urban settings

A distinctive contribution of the WOAS is its contextual grounding for Indian older adults. Unlike Western-developed instruments, the scale incorporates locally relevant environmental conditions, such as broken footpath slabs, stay wires, electrical poles, stray dogs, and open drains hazards. The Kannada translation further enhances accessibility and ecological validity for older adult respondents in Karnataka who may not be proficient in English. The income group categories used in the demographic section including Below Poverty Line (BPL), Economically Weaker Section (EWS), Lower Income Group (LIG), and Middle Income Group (MIG) categories are aligned with Indian government income classification norms, ensuring meaningful socioeconomic segmentation of walkability perceptions.

4.4 Practical implications

The WOAS offers a diagnostic and evaluative tool, benefiting urban planners, public health officials, and policymakers. First, as a perception-based instrument administered directly to older adults, it captures the subjective experience of the walking environment a dimension that objective audit tools cannot provide. Second, the antecedent constructs (GPI, CEP, AUP) provide direct diagnostic insight into policy implementation gaps: low scores on GPI, for instance, would signal that residents do not perceive adequate government investment in pedestrian infrastructure, enabling targeted advocacy and budget allocation. Third, the outcome constructs (IPL, ISL, IQL) enable measurement of the downstream benefits of walkability improvement programmes, supporting evidence-based evaluation of urban design interventions for older adults. The scale can be applied longitudinally to monitor the effectiveness of the urban development initiatives such as, Smart City Mission projects, and Age-Friendly urban development programs across Indian municipalities.

5 Conclusion

This study presents the systematic development and pilot validation of the Walkability of Older Adults Scale (WOAS), a 33-item, 11-construct measurement instrument designed to assess neighbourhood-built environment walkability from the perspective of older adults in Indian urban settings. The scale was developed through a rigorous three-phase process encompassing a systematic review of 217 papers and 95 initial items, five rounds of expert content validation by eight domain experts, and a PLS-SEM pilot testing and analysing the scale demonstrates acceptable reliability, construct validity, and theoretical coherence with 43 older adult respondents in Udupi, Karnataka. Item reduction from 95 to 33 items was guided by factor loading thresholds, with three items retained per construct to ensure adequate latent variable representation.

The pilot study confirmed acceptable reliability (Cronbach’s alpha: 0.5–0.8; Composite Reliability: 0.7–0.8) and largely satisfactory discriminant validity under the Fornell-Larcker criterion. R2 values ranged from 0.109 to 0.683, indicating that antecedent constructs (Government Support to Infrastructure, Community Engagement Programs, Age-Friendly Urban Design Policies) exert meaningful explanatory influence over both walkability dimensions and wellbeing outcomes. The scale’s integrative framework bridging policy enablers, physical environmental attributes, and quality-of-life outcomes represents a novel contribution to walkability measurement literature.

5.1 Limitations

Several limitations should be acknowledged. First, the pilot sample (n = 43) is small and collected through purposive sampling in a single city, limiting its representativeness and the generalisability of the pilot-stage psychometric parameters. Second, convergent validity was not fully achieved across all constructs: AVE values for Aesthetics (AST; AVE = 0.48), Increased Physical Activity Level (IPL; AVE = 0.499), Improved Quality of Life (IQL; AVE = 0.497), and Security (SCT; AVE = 0.419) fell marginally below the recommended threshold of ≥ 0.50 (Fornell and Larcker, 1981), while Enhanced Socialisation (ISL; AVE = 0.543) remained above the threshold. Additionally, the Fornell-Larcker discriminant validity criterion was not fully satisfied for ISL and SCT, indicating that the boundaries between these constructs and conceptually proximate dimensions require further refinement with a larger sample. Third, as a pilot study, the structural model relationships (path coefficients) are indicative rather than confirmatory, and their substantive interpretation should be deferred to the primary data collection phase. Fourth, the scale was developed and translated for one Indian state (Karnataka) and one language (Kannada); its applicability to other Indian linguistic and cultural contexts requires additional translation and validation.

5.2 Future research directions

Future research should focus on large-scale validation using stratified samples across multiple cities. Heterotrait-Monotrait (HTMT) ratio analysis should be conducted alongside the Fornell-Larcker criterion in the full study to provide a more robust test of discriminant validity (Henseler et al., 2015). Multi-group analysis (MGA) in PLS-SEM may be employed to test whether the scale functions equivalently across gender, income, and age sub-groups of older adults. Translation and cross-cultural validation of the WOAS for other Indian languages and metropolitan contexts would enhance generalisability. Finally, integration of the WOAS with objective audit-based walkability measures would enable triangulation of subjective perceptions and objective environmental conditions, strengthening the robustness of findings.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript. The data presented in the manuscript are entirely original. The authors acknowledge the use of AI-based tools, specifically Claud AI (Version Sonnet 4.6), and Americal Journal Experts (AJE), were utilized solely for language editing, grammatical corrections, and improvement of academic clarity in the manuscript. These tools were not used for generating research content, interpreting data, or synthesizing the literature. The conceptualization, analysis, and interpretation of the results were performed entirely by the authors.

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Footnotes

Edited by: Sinan Yılmaz, Atatürk University, Türkiye

Reviewed by: Ralf Strobl, LMU Munich University Hospital, Germany

Dong Wei, Southwest Jiaotong University, China

Autchariya Punyakaew, Chiang Mai University, Thailand

Handan Turkoglu, Retired, Istanbul, Türkiye

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics approval

The studies involving humans were approved by the Kasturba Medical College Institution and Kasturba Hospital (constituent unit of the Manipal Academy of Higher Education, Manipal-576104) and the Institutional Ethics Committee (Registration No. ECR/146/Inst/KA/2013/RR-19, DHR Registration No. EC/NEW/INST/2019/374) for providing ethical clearance (Approval No. IEC1: 387/2022). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

AR: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. NR: Conceptualization, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. SD: Formal analysis, Writing – original draft, Writing – review & editing. RS: Formal analysis, Writing – original draft, Writing – review & editing. KM: Formal analysis, Writing – original draft, Writing – review & editing. JJ: Formal analysis, Writing – original draft, Writing – review & editing.

Acknowledgements

The authors gratefully acknowledge the Kasturba Medical College Institution and Kasturba Hospital (constituent unit of the Manipal Academy of Higher Education, Manipal-576104) and the Institutional Ethics Committee (Registration No. ECR/146/Inst/KA/2013/RR-19, DHR Registration No. EC/NEW/INST/2019/374) for providing ethical clearance (Approval No. IEC1: 387/2022) for this study. We gratefully acknowledge the Manipal Academy of Higher Education for their unwavering support and encouragement at every step of this journey.

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Citation

Rao, A., et al. (2026). Measuring neighbourhood walkability for older adults: development and pilot validation of a multidimensional scale. Frontiers in Sustainable Cities, Volume 8. https://doi.org/
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Copyright © 2026 Rao, Rama Devi, Dixit, Shetty, Mallaiah and John.

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*Correspondence: Akshatha Rao

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