Tools for detecting ageing in people with Autism Spectrum Disorder: A scoping review

Academic Editor: Alasdair Barr Received: 14 July 2025 Revised: 29 August 2025 Accepted: 10 October 2025 Published: 20 October 2025 Citation: Ugartemendia-Yerobi, M.; Pereda-Goikoetxea, B.; Trespaderne, M.I.; Lacalle, J. Tools for Detecting Ageing in People with Autism Spectrum Disorder: A Scoping Review. Healthcare 2025,13, 2640. https:// doi.org/10.3390/healthcare13202640 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Review Tools for Detecting Ageing in People with Autism Spectrum Disorder: A Scoping Review Maider Ugartemendia-Yerobi * , Beatriz Pereda-Goikoetxea , Maria Isabel Trespaderne and Jaione Lacalle * Faculty of Medicine and Nursing, Department of Nursing II, University of the Basque Country, 20014 Donostia-San Sebastian, Spain; [email protected] (B.P.-G.) *Correspondence: maider[email protected] (M.U.-Y.); [email protected] (J.L.) Abstract Background: People with Autism Spectrum Disorder (ASD) require a customised, multidisciplinary plan throughout their lifetime to support optimal health. The purpose of this scoping review was to synthesise research on the main scales used to detect signs of ageing in people with ASD. Methods: Eligible papers published between January 2003 and August 2025 were identified through searches of PubMed, PsycInfo, Scopus, Web of Science, NICE and Cochrane databases. The assessment was performed using the Joanna Briggs Institute critical appraisal and extraction checklist. Of the 820 papers reviewed, 24 were found to meet the established criteria. Results: Based on the evidence collected, 57 tools focusing on specific domains within the Comprehensive Geriatric Assessment were identified: 19 addressed the functional domain, 18 the mental, 6 the biomedical, 1 the social, 2 related to frailty, 1 to fall risk, and 10 to quality of life. Conclusions: This review highlights the need to obtain a ‘multi-domain’ tool for the detection of ageing in autistic people, which would facilitate the development of a Comprehensive Geriatric Assessment that makes planning customised care possible. Keywords: aging; Autism Spectrum Disorder; assessment; frail elderly 1. Introduction It is expected that due to population ageing and the increase in chronic diseases, by 2050, the global population of people over 60 years of age will more than double and reach 2100 million [ 1 ]. The World Report on Ageing and Health acknowledges that increasing longevity depends to a large extent on healthy ageing, that is, on developing and maintaining, even at an advanced age, the functional capacity necessary for well-being [ 2 ]. Although the likelihood of living longer constitutes an important collective achievement, a great inequality in longevity persists depending on the social and economic group to which one belongs. Similarly, authors such as Rowe and Kahn [ 3 ] suggest that people with disabilities may experience ‘unsuccessful ageing’; however, rather than implying their exclusion, this highlights the need for their prioritisation in initiatives aimed at promoting healthy ageing. In this sense, alongside social and economic factors, neurotype should also be considered, as autistic individuals face specific barriers that contribute to increased morbidity and mortality [4]. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects both children and adults [ 5 ]. Currently, the DSM-5 establishes a new classification system based on a single spectrum that better acknowledges the diversity of ASD, characterised by an atypical social and communication style and restricted interests and/or repetitive Healthcare 2025,13, 2640 https://doi.org/10.3390/healthcare13202640 Healthcare 2025,13, 2640 2 of 20 behaviours [ 6 ], areas referred to as core symptoms [ 7 ]. It should also be noted that there is a high comorbidity between ASD and intellectual disability (ID) [ 8 ], affecting approximately 50% of autistic people [9]. Scientific evidence suggests that people with autism begin to experience biopsychosocial changes from the age of 40 onwards. This early deterioration is associated with an increased risk of premature mortality [ 10 ]. Compared to the general population, people with ASD experience poorer outcomes at all stages of the life cycle [ 11 ], with a dementia prevalence of 1.9% in men and 3.2% in women [ 12 ], and a higher incidence of Parkinson’s disease [ 10 ]. Similarly, evidence indicates that individuals with intellectual disabilities begin to experience a decline in their quality of life from approximately 45 to 50 years of age [13]. People with ASD need a customised, lifelong multidisciplinary plan that undergoes constant review and monitoring to support their full potential, social integration, and quality of life [ 14 ]. In this context, the Comprehensive Geriatric Assessment (CGA), a central tool in geriatrics, is an interdisciplinary, dynamic, and multidimensional process that assesses both the capacities and needs of an individual while identifying potential issues. It also facilitates the multidisciplinary diagnostic process and enables care planning focused on enhancing quality of life and maximising overall health for older adults [ 15 , 16 ]. Understanding the progression of age-related changes in people with ASD will thus aid in planning necessary supports for this stage of life [17,18]. Traditionally, tools developed for individuals with intellectual disabilities have been used to assess people with ASD, due to the high comorbidity between both conditions. In this regard, several authors report a notable lack of research on the ageing process and autism [ 7 , 19 ]. Therefore, the objective of this review is to identify the main scales that facilitate the detection of ageing in people with ASD, taking into consideration tools developed both for people with ASD and for those with intellectual disabilities. 2. Methods 2.1. Design and Research Question A scoping review was conducted and the process was reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [ 20 ]. To examine existing scientific evidence, we framed our research question based on the PICO framework [ 21 ]—Population: People with ASD; Intervention: Assessment tools; Comparator: Not applicable; and Outcome: Identification and assessment of ageing process. Therefore, the following research question was formulated: Which tools for the detection of ageing in people with ASD exist? 2.2. Search Strategy The search was conducted in MEDLINE (PubMed), PsycInfo, Scopus, Web of Science (WOS), the National Institute for Health and Care Excellence (NICE), and Cochrane from 1 January 2003 to 15 August 2025, with no language restrictions. Results were updated on 27 August 2025. Based on the terms “Autism Spectrum Disorder”, “Intellectual Disability”, “Aging”, “Frailty”, “Frail Elderly”, and the free term “assessment”, we used the following search command: (((“Autism Spectrum Disorder”[Mesh]) OR (“Intellectual Disability”[Mesh])) AND (((“Aging”[Mesh]) OR (“Frailty”[Mesh])) OR (“Frail Elderly”[Mesh]))) AND (assessment). The search was completed with additional papers obtained through the snowball method with backward citation tracking [ 22 , 23 ]. The search strategies used in various databases are detailed in Supplementary File S1. Healthcare 2025,13, 2640 3 of 20 2.3. Inclusion and Exclusion Criteria The scope of this review was established via inclusion and exclusion criteria (Table 1). Table 1. Inclusion and exclusion criteria. INCLUSION Studies including adults aged 40 years or older with ASD or ID EXCLUSION Pathology: Down syndrome, Alzheimer’s and other dementias, Hunter’s disease, cancer, Rett syndrome, COVID-19 Life stage: childhood stage Type of study: experimental with animals, study protocol, case study, intervention studies Topic of study: brain structures, palliative care in people with intellectual disabilities, caregivers, diagnostic tools and characteristics of ASD, assessment of isolated cognitive function, socio-health resources 2.4. Study Selection The initial search identified 820 papers (PubMed: 187, PsycInfo: 16, Scopus: 299, WOS: 197, NICE: 85, and Cochrane: 36). After removing duplicates, two authors (M.U.- Y. and B.P.-G.) assessed the eligibility of 735 papers (including the 9 identified through snowball sampling). They independently examined the titles and abstracts, eliminating 634 papers for irrelevance. The remaining 62 papers were examined in full text. Two other investigators (J.L. and M.I.T.) resolved disagreements during the study selection process through discussion until consensus was reached. As reported in Figure 1, a total of 24 papers met the eligibility criteria, while a list of the 38 excluded studies is provided in Supplementary File S2. 2.5. Quality Appraisal The Joanna Briggs Institute (JBI) critical appraisal and extraction checklist was used to review and assess the rigor of the included papers [ 24 ]. Two researchers (M.U.-Y. and B.P.-G.) independently reviewed the papers and extracted the data before querying the identified common categories. The main issues were debated until a consensus was reached, and any disagreements between these two researchers were reviewed by two other researchers (J.L.P. and I.T.B.) to maintain the rigor of the review process (Supplementary File S3). 2.6. Data Extraction Prior to data extraction, the protocol for this study was registered in PROSPERO (ID: CRD42024571799). Two authors (M.U.-Y. and B.P.-G.) used a narrative approach to data extraction using a structured template. The data extracted from the selected papers were as follows: author, country, year of publication, design, tools used, participant characteristics, and key research findings (Table 2). Healthcare 2025,13, 2640 4 of 20 Records identified through databases PubMed (n = 187) Scopus (n = 299) WOS (n = 197) PsycInfo (n = 16) Cochrane (n = 36) NICE (n = 85) TOTAL (n = 820) Identification Additional records identified through other sources ( n = 9 ) Records after duplicates removed (n = 735) Screenin g Records screened (n = 696) Records excluded based on title and abstract Pathology: Down Syndrome, Alzheimer’s and other dementias, Hunter’s Disease, cancer, Rett Syndrome, COVID-19 Life stage: childhood Type of study: experimental with animals, study protocol, case study, intervention studies Topic of study focused on: brain structures, palliative care in people with intellectual disabilities, the caregiver, diagnostic tools for ASD, characteristics of ASD, assessment of an isolated cognitive function, socio-health resources (n = 634) Full-text papers excluded (n = 38) Reasons: Intervention studies (n = 4) ASD diagnostic tools (n = 5) Other non-ASD psychiatric illnesses (n = 3) Focused on characteristics of ASD (n = 11) Social and health resources (n = 11) Assessment of a partial function (n = 4) Full-text papers assessed for eligibility (n = 62) Eligibility Studies included in review (n = 24) Included Figure 1. Prisma Flow Diagram of Study Selection. From: [20]. Healthcare 2025,13, 2640 5 of 20 Table 2. Characteristics of the selected studies. Author (Year)/Country Study Design Assessment Participants and Range Years or Mean Age Key Findings Ayres et al. (2018)/UK [25] Systematic review WHOQOL-BREF QoL-Q QOLI ComQOL SF-36 SF-12 v.2 QOL1 and QOL2 N = 959 18–83 years No comprehensive, autism spectrum disorder–specific quality of life measurement tools have been validated. Choi et al. (2020)/Southeast of the United States [26] Cohort study Anthropometrics JHFRAT SF-36 SPPB Accelerometer N = 80 43 ±13 years Adults with ID who experience falls are more likely to need support with ADLs, be older, and have arthritis, rheumatism, and walking problems than adults with ID who do not experience falls. Geurts et al (2020)/Netherlands [27] Cross-sectional WAIS IV D-KEFS BADS Zoo Map WCST Adult BRIEF-A N = 101 60–85 years Subjective measures offer valuable insight into everyday executive functioning and the experienced problems in an ASC population. Groot et al. (2021)/Netherlands [28] Case-control MMSE-NL MoCA-NL N = 100 30–73 years There is no difference in performance between people with and without an ASC on the MMSE-NL or MoCA-NL. Hilgenkamp et al. (2010)/Netherlands [29] Systematic review BBT BBS POMA I Walking speed GS 30 CST MBSSR ISWT Older adults (age is not specified) The following are proposed for measuring physical fitness: BBT, reaction time test, BBS, walking speed, GS, 30 CST, MBSSR, and ISWT. Hwang et al. (2020)/Australia [30]Cross-sectional W-ADL SF-12 WHODAS 2.0 N = 152 40–79 years Significantly less autistic adults were ‘maintaining physical and cognitive functioning’ and ‘actively engaging with life’ in comparison to controls. The current dominant model of ‘ageing well’ is limited for examining autistic individuals. Lever and Geurts (2016)/Netherlands [31] Cross-sectional WAIS III MMSE WMS-III RAVLT COWAT GIT-2 CFQ N = 236 20–79 years Age-related differences characteristic of typical ageing are reduced or parallel, but not increased, in individuals with ASD. Maring et al. (2013)/USA [32] Systematic review and pilot study Barthel Index FIM POMA I 2-MWT N = 30 >50 years The measures are strongly associated and successfully distinguished between participants with an adverse health event in the previous year. Healthcare 2025,13, 2640 6 of 20 Table 2. Cont. Author (Year)/Country Study Design Assessment Participants and Range Years or Mean Age Key Findings Mason et al. (2021)/New Zealand [7] Cohort design Pace of ageing: ageing biomarkers, facial ageing, perceived health N = 915 3–45 years They found that higher autistic traits were associated with poorer physical health and a faster pace of ageing. McConachie et al. (2018)/UK [33]Validation study ASQoLç WHOQoL-BREF N = 309 18–76 years A psychometric validation of the World Health Organization measure WHOQoL-BREF was conducted; additionally, the construct validity of the WHO Disabilities module was examined, and nine additional autism-specific items (ASQoL) were developed based on extensive consultation with the autism community. McKenzie (2016)/Ontario Canada [34] Cohort design RAI-HC N = 3034 18–99 years Frail individuals had greater rates of admission than non-frail individuals. The FI predicts institutionalisation. McKenzie et al. (2015)/Ontario, Canada [35] Cohort study RAI-HC N = 7863 18–99 years Using the FI to identify frailty in adults with IDD is feasible and may be incorporated into existing home care assessments. Miot et al. (2023)/France [8]Cohort design VABS-II Total number of medications DBI Comorbidities DSQIID RSMB N = 63 25–59 years Spectrum disorder + intellectual disability individuals can be identified based on their multimorbidity and potentially different ageing trajectories. Oppewal et al. (2014)/Netherlands [36] Cohort design BBT BBS Walking speed GS 30 CST MBSSR ISWT N = 602 >50 years Physical fitness significantly predicts a decline in daily functioning in older adults with ID. Oppewal et al. (2015)/Netherlands [37] Cohort design BBT BBS Walking speed GS 30 CST MBSSR ISWT Lawton IADL N = 601 >50 years Physical fitness is found to be an important aspect for IADL. Healthcare 2025,13, 2640 7 of 20 Table 2. Cont. Author (Year)/Country Study Design Assessment Participants and Range Years or Mean Age Key Findings Roestorf et al. (2025)/UK [38]Cross-sectional PRMQ EBPM TBPM WHOQOL-BREF N = 57 23–80 years QoL was positively associated with TBPM accuracy in non-autistic participants. In addition to confirming previous findings showing that autistic individuals have more significant difficulties with TBPM compared to EBPM, the results suggest that neither difficulties with EBPM nor TBPM appear to adversely affect their overall or health-related QoL. Schmidt et al. (2015)/Germany [39]Cross-sectional Mini-DIPS WHODAS 2.0 FLZ N = 87 Age: mean = 31 Adults on the autism spectrum without intellectual impairment experience significant functional impairments in social domains, but they are relatively competent in daily living skills. Schoufour et al. (2022)/Netherlands [40] Longitudinal and case series ID-FI ID-FI Short Form N = 982 >50 years A practical tool to assess the frailty status of people with ID is introduced. Schoufour, Echteld, et al. (2015)/Netherlands [41] Cohort design Occurrences of hospitalisation Total number of used medicines Comorbid conditions ID-FI N = 982 >50 years The FI was related to an increased risk of higher medication use and several comorbid conditions, although not to falls, fractures, and hospitalisation. Schoufour, Evenhuis, et al. (2014)/Netherlands [42] Cohort design Barthel Index Lawton IADL ID-FI N = 676 >50 years Increased care during the follow-up was related to a high frailty index score at baseline. Schoufour, Mitnitski, et al. (2014)/Netherlands [43] Cohort design Barthel Index Lawton IADL AI GMFCS ID-FI Pedometer N = 703 >50 years The FI demonstrated the highest predictive value for individuals with high baseline mobility or independence in IADLs. Schoufour, Mitnitski, et al. (2015)/Netherlands [44] Cohort design -ID-FI N = 982 >50 years The predictive validity of the FI was strongly associated with 3-year mortality. Torenvliet et al. (2022)/Netherlands [45] Cohort design RAVLT WMS-III COWAT GIT-2 CFQ N = 176 30–89 years Previously observed difficulties in Theory of Mind and verbal fluency, which appear to persist into older age, were replicated. Healthcare 2025,13, 2640 8 of 20 Table 2. Cont. Author (Year)/Country Study Design Assessment Participants and Range Years or Mean Age Key Findings Torenvliet et al. (2023)/Netherlands [46] Cohort design CFQ WAIS III/IV MMSE N = 464 24–85 years Autistic individuals diagnosed in adulthood, without intellectual disability, do not seem at risk for accelerated cognitive decline. 2-MWT: 2-Minute Walk Test; 30 CST: 30-s Chair Stand Test; ADL: Activities of Daily Living; AI: Hauser Ambulation Index; ASC: Autism Spectrum Condition; ASQoL: Autism-Specific Quality of Life Questionnaire; BADS Zoo Map: The Zoo Map Test of the Behavioural Assessment of the Dysexecutive Syndrome; BBS: Berg Balance Scale; BBT: Box and Block Test; BRIEF-A: Behaviour Rating Inventory of Executive Function–Adult Version; CFQ: Cognitive Failures Questionnaire; ComQOL: Comprehensive Quality of Life Inventory; COWAT: Controlled Oral Word Association; DBI: Drug Burden Index; D-KEFS: Delis–Kaplan Executive Function System; DSQIID: Dementia Screening Questionnaire for Individuals with Intellectual Disabilities; FI: Frailty Index; FIM: Functional Independence Measure; FLZ: Assessment of life satisfaction ‘Fragebogen zur Lebenszufriedenheit’; GIT-2: Groninger Intelligence Test 2; GMFCS: Gross Motor Function Classification Scale; GS: Grip Strength; IADL: Instrumental Activities of Daily Living; IDD: Intellectual and Developmental Disabilities; ID-FI: Intellectual Disability-Frailty Index; ISWT: Incremental Shuttle Walk Test; JHFRAT: The Johns Hopkins Fall Risk Assessment Tool; Lawton IADL: Lawton Instrumental Activities of Daily Living; MBSSR: Modified Back-Saver Sit and Reach; Mini-DIPS: Diagnostic Interview for Mental Disorders—Short Version; MMSE: Folstein Minimental State Examination; MoCA: Montreal Cognitive Assessment; POMA I: Performance-Oriented Mobility Assessment; PRMQ: Prospective and Retrospective Memory Questionnaire; QoL: Quality of Life; QOL1 and QOL2: Novel QoL measures; QOLI: Quality of Life Inventory; QoL-Q: Quality of Life Questionnaire; RAI-HC: Resident Assessment Instrument-Home Care; RAVLT: Rey-Auditory Verbal Learning Test; RSMB: Reiss Screen for Maladaptive Behaviour; SF-12 v.2: Medical Outcomes Study Short-Form Health Survey Version 2; SF-36: Short-Form Health Survey; SPPB: Short Physical Performance Battery; VABS-II: Vineland Adaptive Behaviour Scales, Second Edition; W-ADL: Waisman Activities of Daily Living Scale; WAIS-III, WAIS-IV: Wechsler Adult Intelligence Scale; WCST: Wisconsin Card-Sorting Task; WHO: World Health Organization; WHODAS 2.0: World Health Organization Disability Assessment Schedule 2.0; WHOQOL-BREF: World Health Organization Quality-of-Life Scale; WMS-III: Wechsler Memory Scale. 3. Results Traditionally, CGA has been aimed at identifying four main domains: biomedical, functional, social, and mental. CGA comprises other domains such as frailty, nutritional status, falls, sarcopenia, and quality of life [15]. The CGA tools obtained in the scoping review are shown in Figure 2. A total of 57 tools have been identified, of which 34 have been used in people with ASD and 23 in people with ID. Specifically, 19 tools correspond to the functional domain (1 ASD, 18 ID), 18 to the mental domain (18 ASD), 6 to the biomedical domain (4 ASD, 2 ID), 1 to the social domain (1 ASD), 2 to assess frailty (2 ID), 1 for the risk of falls (1 ID), and 10 for quality of life (10 ASD). Supplementary File S4 presents the availability and key psychometric properties of these scales. Healthcare 2025,13, 2640 9 of 20 Tools used in people with ASD; Tools used in people with ID COMPREHENSIVE GERIATRIC ASSESSMENT FUNCTIONAL ASSESSMENT MENTAL ASSESSMENT BIOMEDICAL ASSESSMENT SOCIAL ASSESSMENT OTHER DOMAINS MMSE MoCA WAIS-III WAIS-IV WMS-III RAVLT COWAT GIT-2 CFQ WHODAS 2.0 DSQIID RSMB Mini-DIPS D-KEFS BADS Zoo Map WCST Adult Version BRIEF-A PRMQ Comorbid conditions Anthropometry Total number of used medicines Occurrences of hospitalization DBI Pace of ageing: ageing biomarkers, facial ageing, and perceived health VABS-II PHYSICAL DISABILITY PHYSICAL CONDITION Fragility Risk of falls Quality of life W-ADL Barthel Index Lawton IADL FIM RAI-HC Walking, Mobility and balance Walking, Mobility and balance Physical activity ID-FI ID-FI Short Form JHFRAT WHOQOLBREF QoL-Q QOLI ComQOL SF-36 SF-12 V.2 QOL1 QOL2 FLZ ASQoL AI GMFCS SPPB 2-MWT POMA I BBS Walking speed 30 CST MBSSR ISWT BBT GS Pedometer Accelerometer Figure 2. Comprehensive geriatric assessment tools identified in the scoping review. Healthcare 2025,13, 2640 16 of 20 Supplementary File S2: List of the Excluded Studies at Full-Text Assessment; Supplementary File S3: JBI’s Critical Appraisal Tools; Supplementary File S4: Key Assessment Tools by Domain: Availability and Psychometric Properties. Author Contributions: Conceptualization and design, J.L.; search strategy, B.P.-G. and M.U.-Y.; study selection, B.P.-G. and M.U.-Y.; data extraction, B.P.-G. and M.U.-Y.; data analysis and synthesis, M.I.T. and J.L.; manuscript drafting, J.L., B.P.-G. and M.U.-Y.; general supervision, M.I.T. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the University of the Basque Country UPV/EHU (GIU22/019) and The Provincial Council of Gipuzkoa (Etorkizuna Eraikiz DGE23/07). Institutional Review Board Statement: Not applicable. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. Acknowledgments: We thank the Autism Association of Gipuzcoa (Gautena) for the support received. Conflicts of Interest: The authors declared no potential conflicts of interest. References 1. World Health Organization. Decade of Healthy Ageing: Plan of Action 2021–2030; World Health Organization: Geneva, Switzerland, 2020; pp. 1–26. Available online: https://www.who.int/initiatives/decade-of-healthy-ageing (accessed on 10 May 2025). 2. Sataloff, R.T.; Johns, M.M.; Kost, K.M. World Report on Ageing and Health. Geneva. 2015. Available online: https://iris.who.int/ bitstream/handle/10665/186463/9789240694811_eng.pdf?sequence=1 (accessed on 13 May 2025). 3. Rowe, J.W.; Kahn, R.L. Successful aging. Gerontologist 1997,37, 433–440. [CrossRef] 4. Stringfellow, M.K.; Fields, N.L.; Lee, K.; Anderson, K.A.; Brokaw, E. Healthy Aging and Older Adults with Autism: A Scoping Review. Gerontologist 2024,64, gnae026. [CrossRef] 5. Lord, C.; Brugha, T.; Charman, T.; Cusack, J.; Dumas, G.; Frazier, T.; Jones, E.; Jones, R.; Pickles, A.; State, M.; et al. Autism spectrum disorder. Nat. Rev. Dis. Prim. 2020,6, 5. [CrossRef] 6. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5-TR), 5th ed.; American Psychiatric Association: Washington, DC, USA, 2022. 7. Mason, D.; Ronald, A.; Ambler, A.; Caspi, A.; Houts, R.; Poulton, R.; Ramrakha, S.; Wertz, J.; Moffitt, T.E.; Happé, F. Autistic traits are associated with faster pace of aging: Evidence from the Dunedin study at age 45. Autism Res. 2021,14, 1684–1694. [CrossRef] [PubMed] 8. Miot, S.; Chancel, R.; Peries, M.; Crepiat, S.; Couderc, S.; Pernon, E.; Picot, M.C.; Gonnier, V.; Jeandel, C.; Blain, H.; et al. Multimorbidity patterns and subgroups among autistic adults with intellectual disability: Results from the EFAAR study. Autism 2023,27, 762–777. [CrossRef] [PubMed] 9. World Health Organization. Questions & Answers on Autism Spectrum Disorders (ASD) [Internet]. 2022. Available online: https://www.who.int/news-room/questions-and-answers/item/autism-spectrum-disorders-(asd) (accessed on 9 July 2025). 10. Bishop-Fitzpatrick, L.; Rubenstein, E. The physical and mental health of middle aged and older adults on the autism spectrum and the impact of intellectual disability. Res. Autism Spectr. Disord. 2019,63, 34–41. [CrossRef] [PubMed] 11. Kuo, S.S.; Van der Merwe, C.; Fu, M.E.; Carey, J.M.; Talkowski, C.E.; Bishop, S.L.; Robinson, E.B. Developmental variability in autism across 17000 autistic individuals and 4000 siblings without an autism diagnosis: Comparisons by cohort, intellectual disability, genetic etiology, and age at diagnosis. JAMA Pediatr. 2022,176, 915–923. [CrossRef] 12. Croen, L.; Zerbo, O.; Qian, Y.; Massolo, M.; Rich, S.; Sidney, S.; Kripke, C. The health status of adults on the autism spectrum. Autism 2015,19, 814–823. [CrossRef] 13. World Health Organization. Healthy Ageing: Adults with Intellectual Disabilities: Summative Report; World Health Organization: Geneva, Switzerland, 2000. Available online: https://iris.who.int/handle/10665/66367 (accessed on 13 May 2025). 14. Reza-Goyanes, M. Efectividad Terapias Conductuales en los Trastornos del Espectro Autista; Plan de Calidad para el SNS del MSSSI; Unidad de Evaluación de Tecnologías Sanitarias, Agencia Laín Entralgo. Informes de Evaluación de Tecnologías Sanitarias: Madrid, Spain, 2012. Available online: http://www.madrid.org/bvirtual/BVCM017399.pdf (accessed on 5 October 2025). 15. Soler, P.A.; Gutiérrez, C.C. Medicina Geriátrica. Una Aproximación Basada en Problemas, 2nd ed.; Elsevier Health Sciences Spain: Barcelona, Spain, 2020. Healthcare 2025,13, 2640 17 of 20 16. Pilotto, A.; Cella, A.; Pilotto, A.; Daragjati, J.; Veronese, N.; Musacchio, C.; Mello, A.; Logroscino, G.; Padovani, A.; Prete, C.; et al. Three decades of comprehensive geriatric assessment: Evidence coming from different healthcare settings and specific clinical conditions. J. Am. Med. Dir. Assoc. 2017,18, 192.e1–192.e11. [CrossRef] 17. Nylander, L.; Axmon, A.; Björne, P.; Ahlström, G.; Gillberg, C. Older adults with autism spectrum disorders in Sweden: A register study of diagnoses, psychiatric care utilization and psychotropic medication of 601 individuals. J. Autism Dev. Disord. 2018,48, 3076–3085. [CrossRef] 18. Wise, E.A. Aging in autism spectrum disorder. Am. J. Geriatr. Psychiatry 2020,28, 339–349. [CrossRef] [PubMed] 19. Wright, S.D.; Wright, C.A.; D’Astous, V.; Wadsworth, A.M. Autism aging. Gerontol. Geriatr. Educ. 2016,40, 322–338. [CrossRef] [PubMed] 20. Tricco, A.C.; Lilliee, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann. Intern. Med. 2018,169, 467–473. [CrossRef] 21. Huang, X.; Lin, J.; Demner-Fushman, D. Evaluation of PICO as a knowledge representation for clinical questions. AMIA Annu. Symp. Proc. 2006,2006, 359–363. [PubMed] 22. Armstrong, R.; Jackson, N.; Doyle, J.; Waters, E.; Howes, F. It’s in your hands: The value of handsearching in conducting systematic reviews of public health interventions. J. Public Health 2005,27, 388–391. [CrossRef] 23. Jalali, S.; Wohlin, C. Systematic literature studies: Database searches vs. backward snowballing. In Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, Lund, Sweden, 20–21 September 2012; pp. 29–38. [CrossRef] 24. Aromataris, E.; Munn, Z. JBI Reviewer’s Manual; JBI: Adelaide, Australia, 2020. [CrossRef] 25. Ayres, M.; Parr, J.R.; Rodgers, J.; Mason, D.; Avery, L.; Flynn, D. A systematic review of quality of life of adults on the autism spectrum. Autism 2018,22, 774–783. [CrossRef] 26. Choi, P.; Wei, T.; Motl, R.W.; Agiovlasitis, S. Risk factors associated with history of falls in adults with intellectual disability. Res. Dev. Disabil. 2020,106, 103748. [CrossRef] 27. Geurts, H.M.; Pol, S.E.; Lobbestael, J.; Simons, C.J.P. Executive functioning in 60+ autistic males: The discrepancy between experienced challenges and cognitive performance. J. Autism Dev. Disord. 2020,50, 1380–1390. [CrossRef] 28. Groot, I.Z.; Lever, A.G.; Koolschijn, P.C.; Geurts, H.M. Brief Report: Using Cognitive Screeners in Autistic Adults. J. Autism Dev. Disord. 2021,51, 3374–3379. [CrossRef] 29. Hilgenkamp, T.I.M.; van Wijck, R.; Evenhuis, H.M. Physical fitness in older people with ID-Concept and measuring instruments: A review. Res. Dev. Disabil. 2010,31, 1027–1038. [CrossRef] 30. Hwang, Y.I.; Foley, K.R.; Trollor, J.N. Aging Well on the Autism Spectrum: An Examination of the Dominant Model of Successful Aging. J. Autism Dev. Disord. 2020,50, 2326–2335. [CrossRef] 31. Lever, A.G.; Geurts, H.M. Age-related differences in cognition across the adult lifespan in autism spectrum disorder. Autism Res. 2016,9, 666–676. [CrossRef] 32. Maring, J.R.; Costello, E.; Birkmeier, M.C.; Richards, M.; Alexander, L.M. Validating functional measures of physical ability for aging people with intellectual developmental disability. Am. J. Intellect. Dev. Disabil. 2013,118, 124–140. [CrossRef] 33. McConachie, H.; Mason, D.; Parr, J.R.; Garland, D.; Wilson, C.; Rodgers, J. Enhancing the Validity of a Quality of Life Measure for Autistic People. J. Autism Dev. Disord. 2018,48, 1596–1611. [CrossRef] 34. McKenzie, K.; Ouellette-Kuntz, H.; Martin, L. Frailty as a predictor of institutionalization among adults with intellectual and developmental disabilities. Intellect. Dev. Disabil. 2016,54, 123–135. [CrossRef] 35. McKenzie, K.; Ouellette-Kuntz, H.; Martin, L. Using an accumulation of deficits approach to measure frailty in a population of home care users with intellectual and developmental disabilities: An analytical descriptive study Public health, nutrition and epidemiology. BMC Geriatr. 2015,15, 170. [CrossRef] 36. Oppewal, A.; Hilgenkamp, T.I.M.; van Wijck, R.; Schoufour, J.D.; Evenhuis, H.M. Physical fitness is predictive for a decline in daily functioning in older adults with intellectual disabilities: Results of the HA-ID study. Res. Dev. Disabil. 2014,35, 2299–2315. [CrossRef] [PubMed] 37. Oppewal, A.; Hilgenkamp, T.I.M.; van Wijck, R.; Schoufour, J.D.; Evenhuis, H.M. Physical fitness is predictive for a decline in the ability to perform instrumental activities of daily living in older adults with intellectual disabilities: Results of the HA-ID study. Res. Dev. Disabil. 2015,41–42, 76–85. [CrossRef] [PubMed] 38. Roestorf, A.; Bowler, D.M.; Gaigg, S.B.; Howlin, P. Prospective memory and quality of life in older and younger autistic adults. Cortex 2025,185, 31–49. [CrossRef] 39. Schmidt, L.; Kirchner, J.; Strunz, S.; Bro´zus, J.; Ritter, K.; Roepke, S.; Dziobek, I. Psychosocial Functioning and Life Satisfaction in Adults with Autism Spectrum Disorder Without Intellectual Impairment. J. Clin. Psychol. 2015,71, 1259–1268. [CrossRef] [PubMed] Healthcare 2025,13, 2640 18 of 20 40. Schoufour, J.D.; Oppewal, A.; van Maurik, M.C.; Hilgenkamp, T.I.M.; Elbers, R.G.; Maes-Festen, D.A.M. Development and validation of a shortened and practical frailty index for people with intellectual disabilities. J. Intellect. Disabil. Res. 2022,66, 240–249. [CrossRef] 41. Schoufour, J.D.; Echteld, M.; Bastiaanse, L.; Evenhuis, H. The use of a frailty index to predict adverse health outcomes (falls, fractures, hospitalization, medication use, comorbid conditions) in people with intellectual disabilities. Res. Dev. Disabil. 2015,38, 39–47. [CrossRef] [PubMed] 42. Schoufour, J.; Evenhuis, H.; Echteld, M. The impact of frailty on care intensity in older people with intellectual disabilities. Res. Dev. Disabil. 2014,35, 3455–3461. [CrossRef] [PubMed] 43. Schoufour, J.D.; Mitnitski, A.; Rockwood, K.; Hilgenkamp, T.I.M.; Evenhuis, H.M.; Echteld, M.A. Predicting disabilities in daily functioning in older people with intellectual disabilities using a frailty index. Res. Dev. Disabil. 2014,35, 2267–2277. [CrossRef] 44. Schoufour, J.D.; Mitnitski, A.; Rockwood, K.; Evenhuis, H.M.; Echteld, M.A. Predicting 3-year survival in older people with intellectual disabilities using a frailty index. J. Am. Geriatr. Soc. 2015,63, 531–536. [CrossRef] 45. Torenvliet, C.; Groenman, A.P.; Radhoe, T.A.; van Rentergem, J.A.A.; Van der Putten, W.J.; Geurts, H.M. Parallel age-related cognitive effects in autism: A cross-sectional replication study. Autism Res. 2022,15, 507–518. [CrossRef] 46. Torenvliet, C.; Groenman, A.P.; Radhoe, T.A.; van Rentergem, J.A.A.; Van der Putten, W.J.; Geurts, H.M. A longitudinal study on cognitive aging in autism. Psychiatry Res. 2023,321, 115063. [CrossRef] [PubMed] 47. Maenner, M.J.; Smith, L.E.; Hong, J.; Makuch, R.; Greenberg, J.S.; Mailick, M.R. Evaluation of an activities of daily living scale for adolescents and adults with developmental disabilities. Disabil. Health J. 2013,6, 8–17. [CrossRef] 48. Mahoney, F.I.; Barthel, D.W. Functional evaluation: The Barthel index. Md. State Med. J. 1965,14, 61–65. 49. Lawton, M.; Brody, E. Assessment of older people: Self-maintaining and instrumental activities daily living. Gerontologist 1969,9, 179–186. [CrossRef] 50. Shah, S.; Vanclay, F.; Cooper, B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J. Clin. Epidemiol. 1989,42, 703–709. [CrossRef] 51. Linacre, J.; Heinemann, A.; Wright, B.; Granger, C.; Hamilton, B. The structure and stability of the Functional Independence Measure. Arch. Phys. Med. Rehabil. 1994,75, 127–132. [CrossRef] [PubMed] 52. Morris, J.; Fries, B.; Bernabei, R.; Steel, K.; Ikegami, N.; Carpenter, I.; Gilgen, R.; DuPasquier, J.; Friters, J.; Henrard, D.; et al. interRAI Home Care (HC) Assessment Form and User’s Manual, 9.1 ed.; Interrail: Washington, DC, USA, 2009. 53. Wagner, A.; Schaffert, R.; Möckli, N.; Zúñiga, F.; Dratva, J. Home care quality indicators based on the Resident Assessment Instrument-Home Care (RAI-HC): A systematic review. BMC Health Serv. Res. 2020,20, 366. [CrossRef] 54. Hauser, S.L.; Dawson, D.M.; Lehrich, J.R.; Beal, M.F.; Kevy, S.V.; Propper, R.D.; Mills, J.A.; Weiner, H.L. Intensive immunosuppression in progressive multiple sclerosis. A randomized, three-arm study of high-dose intravenous cyclophosphamide, plasma exchange, and ACTH. N. Engl. J. Med. 1983,308, 173–180. [CrossRef] 55. Palisano, R.; Rosenbaum, P.; Walter, S.; Russell, D.; Wood, E.; Galuppi, B. Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev. Med. Child. Neurol. 1997,39, 214–223. [CrossRef] [PubMed] 56. Guralnik, J.M.; Ferrucci, L.; Simonsick, E.M.; Salive, M.E.; Wallace, R.B. Lower-Extremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability. N. Engl. J. Med. 1995,332, 556–562. [CrossRef] [PubMed] 57. Butland, R.J.; Pang, J.; Gross, E.; Woodcock, A.A.; Geddes, D.M. Two-, six-, and 12-min walking tests in respiratory disease. Br. Med. J. 1982,284, 1607–1608. [CrossRef] 58. Tinetti, M.E. Performance-oriented assessment of mobility problems in elderly patients. J. Am. Geriatr. Soc. 1986,34, 119–126. [CrossRef] 59. Tinetti, M.E. Factors associated with serious injury during falls by ambulatory nursing home residents. J. Am. Geriatr. Soc. 1987, 35, 644–648. [CrossRef] 60. Bohannon, R.W. Comfortable and maximum walking speed of adults aged 20-79 years: Reference values and determinants. Age Ageing. 1997,26, 15–19. [CrossRef] [PubMed] 61. Rikli, R.E.; Jones, C.J. Senior Fitness Test Manual, 2nd ed.; Human kinetics: Champaign, IL, USA, 2013. 62. Hui, S.S.C.; Yuen, P.Y. Validity of the modified back-saver sit-and-reach test: A comparison with other protocols. Med. Sci. Sports Exerc. 2000,32, 1655–1659. [CrossRef] [PubMed] 63. Singh, S.J.; Morgan, M.D.L.; Scott, S.; Walters, D.; Hardman, A.E. Development of a shuttle walking test of disability in patients with chronic airways obstruction. Thorax 1992,47, 1019–1024. [CrossRef] 64. Mathiowetz, V.; Volland, G.; Kashman, N.; Weber, K. Adult Norms for the Box and Block Test of Manual Dexterity. Am. J. Occup. 1985,39, 386–391. [CrossRef] 65. Mathiowetz, V.; Kashman, N.; Volland, G.; Weber, K.; Dowe, M.; Rogers, S. Grip and pinch strength: Normative data for adults. Arch. Phys. Med. Rehabil. 1985,66, 69–74. 66. Folstein, M.F.; Folstein, S.E.; McHugh, P. ‘Mini-mental state’ A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Researc. 1975,12, 189–198. [CrossRef] Healthcare 2025,13, 2640 19 of 20 67. Nasreddine, Z.S.; Phillips, N.A.; Bédirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.; Chertkow, H. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 2005,53, 695–699. [CrossRef] [PubMed] 68. Wechsler, D. Wechsler Adult Intelligence Scale (WAIS-III); Psychological Corporation: San Antonio, TX, USA, 1997. 69. Wechsler, D. Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV); Pearson Prentice Hall: San Antonio, TX, USA, 2003. 70. Wechsler, D. Wechsler Memory Scale (WMS-III); Psychological Corporation: San Antonio, TX, USA, 1997. 71. Rey, A. L’examen clinique en Psychologie (The Clinical Psychological Examination); Pressee Universitaires de France: Paris, France, 1964. 72. Benton, A.L. Differential behavioral effects in frontal lobe disease. Neuropsychologia 1968,6, 53–60. [CrossRef] 73. Luteijn, F.; Barelds, D.P.H. Groningen Intelligence Test 2 (GIT-2): Manual; Pearson Assessment: Amsterdam, The Netherlands, 2004. 74. Broadbent, D.E.; Cooper, P.F.; FitzGerald, P.; Parkes, K.R. The cognitive failures questionnaire (CFQ) and its correlates. Br. J. Clin. Psychol. 1982,21, 1–16. [CrossRef] 75. Üstün, T.B.; Chatterji, S.; Kostanjsek, N.; Rehm, J.; Kennedy, C.; Epping-Jordan, J.; Saxena, S.; von Korff, M.; Pull, C. Developing the world health organization disability assessment schedule 2.0. Bull. World Health Organ. 2010,88, 815–823. [CrossRef] [PubMed] 76. Deb, S.; Hare, M.; Prior, L.; Bhaumik, S. Dementia screening questionnaire for individuals with intellectual disabilities. Br. J. Psychiatry 2007,190, 440–444. [CrossRef] [PubMed] 77. Reiss, S. Reiss Screen for Maladaptive Behavior; IDS Publishing Corporation: McLean, VA, USA, 1987. 78. Margraf, J. Mini-DIPS-Diagnostisches Kurz-Interview bei psychischen Störungen (Diagnostic Interview for Mental Disorders-Short Version); Springer: Berlin/Heidelberg Germany, 1994. 79. Delis, D.C.; Kaplan, E.; Kramer, J.H. Delis-Kaplan Executive Function System (D–KEFS) [Database Record]; APA PsycTests; APA PsycNet Direct: Washington, DC, USA, 2001. [CrossRef] 80. Wilson, B.A.; Alderman, N.; Burgess, P.W.; Emslie, H.C.; Evans, J.J. The Behavioural Assessment of the Dysexecutive Syndrome; Thames Valley Test Company: Flempton, UK, 1996. 81. Wilson, B.A.; Evans, J.J.; Emslie, H.; Alderman, N.; Burgess, P. The development of an ecologically valid test for assessing patients with a dysexecutive syndrome. Neuropsychol. Rehabil. 1998,8, 213–228. [CrossRef] 82. Grant, D.A.; Berg, E. A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem. J. Exp. Psychol. 1948,38, 404–411. [CrossRef] 83. Roth, R.M.; Isquith, P.K.; Gioia, G. BRIEF-A: Behavior Rating Inventory of Executive Function-Adult Version: Profesional Manual; PAR: Odessa, FL, USA, 2005. 84. Smith, G.; Della Sala, S.; Logie, R.H.; Maylor, E.A. Prospective and retrospective memory in normal ageing and dementia: A questionnaire study. Memory 2000,8, 311–321. [CrossRef] [PubMed] 85. Byrne, C.J.; Walsh, C.; Cahir, C.; Ryan, C.; Williams, D.J.; Bennett, K. Anticholinergic and sedative drug burden in communitydwelling older people: A national database study. BMJ Open 2018,8, e022500. [CrossRef] 86. Belsky, D.W.; Caspi, A.; Houts, R.; Cohen, H.J.; Corcoran, D.L.; Danese, A.; Harrington, H.; Israel, S.; Levine, M.E.; Schaefer, J.D.; et al. Quantification of biological aging in young adults. Proc. Natl. Acad. Sci. USA 2015,112, E4104–E4110. [CrossRef] [PubMed] 87. Elliott, M.L.; Caspi, A.; Houts, R.M.; Ambler, A.; Broadbent, J.M.; Hancox, R.J.; Harrington, H.; Hogan, S.; Keenan, R.; Knodt, A.; et al. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Nat. Aging 2021,1, 295–308. [CrossRef] 88. Sparrow, S.; Balla, D.; Cichetti, D. The Vineland Adaptive Behavior Scales: Interview Edition, Expanded Form; American Guidance Service: Circle Pines, MN, USA, 1984. 89. Poe, S.S.; Cvach, M.; Dawson, P.B.; Straus, H.; Hill, E.E. The Johns Hopkins fall risk assessment tool: Postimplementation evaluation. J. Nurs. Care Qual. 2007,22, 293–298. [CrossRef] [PubMed] 90. The WHOQOL Group. Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychol. Med. 1998,28, 551–558. [CrossRef] 91. Schalock, R.L.; Keith, K.D. Quality of Life Questionnaire (QOL.Q) [Database Record]; APA PsycTests Database Rec.; APA PsycNet Direct: Washington, DC, USA, 1993. [CrossRef] 92. Frisch, M.B.; Cornell, J.; Villanueva, M.; Retzlaff, P.F. Clinical validation of the Quality of Life Inventory. A measure of life satisfaction for use in treatment planning and outcome assessment. Psychol. Assess. 1992,4, 92–101. [CrossRef] 93. Cummins, R.A. Comprehensive Quality of Life Scale-Intellectual/Cognitive Disability Fifth Edition (ComQol-I5), 1997. Available online: http://sid.usal.es/idocs/F5/EVA66/ComQol_I5.pdf (accessed on 7 February 2025). 94. Ware, J.E.; Sherbourne, C.D. The MOS 36-item short-form health survey (SF-36). Med. Care 1992,30, 473–483. [CrossRef] 95. Schmidt, S.; Vilagut, G.; Garin, O.; Cunillera, O.; Tresserras, R.; Brugulat, P.; Mompart, A.; Medina, A.; Ferrer, M.; Alonso, J. Reference guidelines for the 12-Item Short-Form Health Survey version 2 based on the Catalan general population. Med. Clin. 2012,139, 613–625. [CrossRef] [PubMed] Healthcare 2025,13, 2640 20 of 20 96. Billstedt, E.; Gillberg, I.C.; Gillberg, C. Aspects of quality of life in adults diagnosed with autism in childhood: A population-based study. Autism 2011,15, 7–20. [CrossRef] [PubMed] 97. Fahrenberg, J.; Myrtek, M.; Schumacher, J.; Brähler, E. Fragebogen zur Lebenszufriedenheit (FLZ) Handanweisungen. Life Satisfaction Questionnaire; Hogrefe: Göttingen, Germany, 2000. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.