Adaptation and validations of Amsterdam instrumental activities of daily living questionnaire-short version for Peru

Abstract

Background

Instrumental Activities of Daily Living (IADL) are key indicators of functional decline in dementia, but culturally adapted tools for Latin America are scarce.

Objective

This study aims to assess the validity and robustness of a new Peruvian version of the Amsterdam Instrumental Activities of Daily Living Questionnaire-Short Version (A-IADL-Q-SV-P).

Methods

We recruited 171 participants from a cognitive clinic in Peru (81 Clinical Dementia Rating (CDR) = 0, 70 CDR = 1, 20 CDR = 2), including 54 with Alzheimer's disease and 36 with frontotemporal dementia. Validity of the A-IADL-Q-SV-P was assessed using quadratic discriminant analysis to classify CDR level. Spearman correlations tested robustness with demographics (age, sex, education) and associations with cognitive (Mini-Mental State Examination), mood (Generalized Anxiety Disorder-7, Geriatric Depression Scale, and Neuropsychiatric Inventory), and functionality (Pfeffer Functional Activities Questionnaire and Technology-Activities of Daily Living Questionnaire) measures.

Results

The A-IADL-Q-SV-P achieved excellent accuracy for classifying dementia severity (accuracy: 0.853, AUC > 0.92). The ability to classify normal cognition had a sensitivity of 0.95 and a specificity of 0.91, and results were similar for patients with Alzheimer's disease or frontotemporal dementia (accuracy > 0.88, sensitivity > 0.95, and specificity > 0.88, AUC > 0.91). We also found weak associations with age, sex, and education (|rho| < 0.2). Furthermore, the A-IADL-Q-SV-P had strong associations with other clinical scales (|rho| > 0.4), particularly in relation to cognition (rho = −0.70) and functionality (rho > 0.87).

Conclusions

A-IADL-Q-SV-P demonstrated excellent accuracy in classifying dementia severity, with strong correlations to cognitive and other functionality measures. This supports the utility of A-IADL-Q-SV-P as a culturally adapted tool for assessing functional decline in dementia.

Keywords

activities of daily living Alzheimer's disease dementia neuropsychological assessment quality of life

Introduction

Accurate assessment of functional abilities is key in the clinical diagnosis of dementias to determine the clinical status and severity of a patient's disease. Functional dependence is primary criteria for discriminating dementia from mild cognitive impairment (MCI).¹ However, the boundary between the two so far is arbitrary and there is no consensus regarding the precise nature of the loss of activities of daily living (ADL) that is expected to be found in dementia.² Furthermore, ability to perform ADLs change with normal aging³ and the difficulty of certain ADLs may be dependent on culture. ADLs can be classified into basic ADLs (B-ADLs), instrumental (I-ADLs) and advanced (A-ADLs). As cognitive decline progresses, A-ADLs are compromised first, then I-ADLs, and finally B-ADLs.⁴ B-ADLs require low cognitive demands related to self-maintenance (e.g., hygiene, toilet training, commuting, and eating).⁵ I-ADLs require increased cognitive demand, organization, and complex activities related to the ability to live independently in the community (e.g., phone use, transportation, medication management, shopping, and financial management)⁶; while A-ADLs are related to more complex activities related to leisure and occupation.⁷

The relevance of specific ADLs has been found to vary depending on the age, gender, literacy, education, economic and cultural background of subjects.⁸ For example, in some cultures, younger members are in charge of the household's financial aspects, while older adults play a more social role in the community⁹ and the domestic activities of washing, ironing or cooking are typical of women in the household.¹⁰ Thus, tools that measure I-ADLs focused on financial management or household chores may not be adequate, while tools that focus on social activities, such as presiding over ceremonies, may better reflect an individual's functional abilities.⁹ In addition, some LMICs have unique activities that reflect distinct cultural practices and would be considered I-ADL (e.g., appropriately dressing the diverse and numerous garments of the lliclla and Skirts of the highlands of Peru and Bolivia), while its equivalent in Western culture would be characterized as B-ADL (e.g., dressing). Another example is the complex cognitive task of selling candy or vegetables on the street, which is an A-ADLs in many cities in Latin America, but it could be considered an I-ADL in other contexts.¹¹ Therefore, context-specific functional assessments are needed.

In Spanish-speaking Latin American (LA) countries, previous research on aging and dementia in LA has been primarily focused on cognitive screening with limited attention to functional activities.⁸ To date, commonly used functional assessments include the Pfeffer Functional Activities Questionnaire (PFAQ)¹² and Technology-Activities of Daily Living Questionnaire (T-ADLQ).¹³ In Peru, several studies carried out in various communities have used PFAQ,¹⁴ which does not include the evaluation of A-ADLs, limiting its use in clinical practice. Similarly, while the T-ADLQ has been validated for populations with high and medium levels of education,¹⁵ there is a lack of validation for low-education populations, which are common in low and middle-income countries (LMICs). Also the T-ADLQ has been ineffective for differentiating MCI and Alzheimer's disease (AD) dementia.¹⁶ There is a lack of brief functional screening tools to identify early markers of disease or differentiate between healthy adults, those with MCI, and those with dementia in Peruvian populations.

One of the available tools that seems to be most sensitive for detecting alterations in I-ADLs from early-stage AD is the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) that incorporates evaluation of I-ADLs and some items of A-ADLs such as the routine use of modern technologies,¹⁷ social engagement and computer usage patterns.¹⁸ The A-IADL-Q has been shown to be effective in cross-sectional¹⁹ and longitudinal²⁰ studies and has been recommended for research and clinical purposes by the European Joint Program for Neurodegenerative Diseases Working Group.²¹ The original version of 70-items has been adapted and validated in various countries in Europe,¹⁹ and a shorter 30-items version called Amsterdam IADL Questionnaire Short Version (A-IADL-Q-SV) has been adapted and validated in Switzerland²² and has a Spanish version.²³ The Spanish version has been validated in Spain, and showed excellent discrimination between controls and dementia,²³ and was shown to be robust in diverse populations.¹⁹ However, the A-IADL-Q-SV has not been validated in an LMIC, specifically LA.

The objective of the present study was to evaluate and validate the robustness of the adapted Peruvian version of A-IADL-Q-SV (A-IADL-Q-SV-P) to classify controls and dementia in participants with middle and higher-level education in Lima, Peru. Our study aims to provide a culturally and clinically validated tool to improve early detection and functional assessment of dementia in Peru and other Spanish speaking LA countries. By establishing the A-IADL-Q-SV-P's diagnostic accuracy and utility, the study supports more precise, locally relevant care planning and resource allocation in cognitive clinics.

Methods

Study design and participants

We conducted an observational, cross-sectional study that included participant-caregiver pair recruited from research unit of the Instituto Peruano de Neurociencias (IPN), a premier neurodegenerative center in Peru with substantial expertise in leading and managing cognitive research studies, including recruitment.²⁴ Participant recruitment occurs via a well-established referral network in Lima and through community-based outreach and advertisements. The participants were evaluated to complete clinical, neurocognitive and functionality assessment, meanwhile caregivers were interviewed to complete socio-demographic data and functional activities of participants. The pairs were enrolled consecutively between May and December 2023. Eligibility criteria included: (1) participants over 50 years of age, (2) classification as healthy control or dementia after comprehensive evaluation by neurologists and neuropsychologists with expertise in dementia (see diagnostic process and final classification), (3) the informal caregiver was a family members or close friends who had direct contact with the participant for more than 6 h each day for 3 consecutive months prior to the interview), (4) caregiver had the ability to complete the questionnaire, (5) both participant and caregiver had Spanish spoken as a primary language, and (6) both participant and caregiver had obtained a minimum of 6 years of formal education. Participants whose cognitive function may have been impaired by the use of certain drugs or by a particular medical condition, including history of addiction and substance abuse, depression, hypothyroidism, vitamin B12 deficiency, chronic kidney or liver disease, HIV or syphilis neuro-infections, severe head trauma, and subdural hematoma, were excluded. Additionally, those with a condition that could affect their performance in the cognitive evaluation were excluded. These conditions included hearing and visual impairments, motor sequelae of cerebrovascular disorder or traumatic sequelae. Demographic variables including age, sex, and years of education were obtained for each participant. Years of education was established using the question: “How many years did you go to school?”.

The A-IADL-Q-SV-P

The A-IADL-Q-SV is a 30-items questionnaire that takes 10 to 15 min to be completed by the caregiver through an evaluator-led interview. This tool evaluates various I-ADLs as well as activities related to the day-to-day use of technology, including household activities (e.g., shopping or cooking), use of household appliances (e.g., using the microwave or dishwasher), handling of finances (e.g., paying bills or using cash or ATM), work activities, use of the computer (e.g., use of the Internet or word processing programs), use of household appliances (e.g., use of remote control, remote control, or new appliances) as well as leisure activities (e.g., driving or board games).²⁵ Each of the items is scored on a 5-point scale, with responses ranging from “no difficulty” to “unable to perform” (score from 0 to 4). For each item, caregivers are asked if they performed household chores during the past week. If they answer no, they are asked if the reason is due to their cognitive problems. If the answer is “yes, due to cognitive problems”, they are assigned a score of 4; otherwise, an “N/A” is assigned. If the person did perform household chores, they are asked if they find the task more difficult than in the past, with answers that can be: no (0 points), slightly more difficult (1 point), more difficult (2 points), much more difficult (3 points), unable to perform it (4 points). The final score is the average of the scores of the questions that were not “N/A”. This score is multiplied by 25 to get the total score out of 100, instead of 4. Thus, scores of 0–25 indicate minimal problems, scores of 25–50 indicate mild problems, scores of 50–75 indicate moderate problems, and scores of 75–100 indicate serious problems. We used the average score of responses, which is different from the item response theory approach suggested by the developers. However, item response theory is difficult to implement clinically, as they use a proprietary formula from the developers, and are not adjusted for culturally specific contexts. This average score approach has been shown to have psychometric validity and high concordance with the item response theory scoring.²⁶

Cultural adaptations and pilot

The Spanish version of A-IADL-Q-SV which can be obtained free of charge from the developers (https://www.alzheimercentrum.nl/professionals/amsterdam-iadl/). To adapt the A-IADL-Q-SV to a Peruvian version (A-IADL-Q-SV-P), we first pre-tested the questionnaire on clinicians and 10 caregivers to assess the comprehensibility, highlight any items that may be inappropriate at a conceptual level, and identify any other issues that may cause confusion (e.g., unclear wording). Additionally, six clinicians (1 neurologist, 1 neuro-physiatrist, and 4 neuropsychologists) from the research unit were asked to give feedback on the A-IADL-Q-SV-P. As a result, small adjustments were made and documented. Such adjustments included the correction of spelling mistakes, grammatical inaccuracies, and improved clarity of wording. We performed 5 cultural modifications: item 12 (“teléfono móvil” was used for “teléfono celular”), item 13 (“papeleo doméstico” was used for “gastos domésticos”), item 15 (“codigo PIN” was used for “contraseña”); items 21 and 23 (“ordenador” was used for “computadora”), and item 25 (“mando a distancia” was used for “control remoto”).

After modification, a pilot study of the A-IADL-Q-SV-P was performed on a separate cohort of 30 caregivers of people with MCI or dementia (average age 67 and secondary level of schooling). A “thinking-out-loud” method was used, where we documented verbal comments from informants about the relevance of each item, the applicability/meaning of the activities, and the understandability of the questions. Additionally, the completed questionnaires were examined to detect high levels of missing items or single responses. Minor adjustments were again made to the questionnaire and discussed with the developer. Points of discussion included the specification of items, e.g., item 20 “trabajo” was supplemented with the specification “remunerado o no remunerado” particularly in women; or for item 28 “sistema de navegación” supplemented with the “por ejemplo uso de Waze.” Accordingly, a final version of the A-IADL-Q-SV-P was obtained.

Diagnostic process and final classification

The evaluations were carried out at research unit of the IPN. Each participant undertook a standardized clinical and neuropsychological evaluation¹⁶ (see Neurocognitive and functional assessment). During these same sessions, the informants were interviewed to complete the A-IADL-Q-SV-P.

Participants were referred from the neurology offices of the IPN, where each participant in cognitive evaluation is subjected to a process of 3 successive phases: (1) screening – to detect cognitive impairment, (2) disease classification – diagnostic testing to rule out other causes of dementia, and (3) final classification – dementia sub-type and severity of the disease.²⁷ In the screening phase we used the brief cognitive test, RUDAS to screen for cognitive impairment. If the participant was classified with cognitive impairment, they move on to the second phase to be evaluated for reversible or non-neurodegenerative causes of dementia using blood tests (i.e., hemoglobin, glucose, transaminase, urea, creatinine, vitamin B12, folic acid, free T3 and free T4, ultrasensitive thyroid stimulating hormone, rapid plasma reagin test, and an enzyme-linked immunosorbent assay for HIV), brain magnetic resonance imaging (MRI), and Beck Depression Inventory II (BDI-II). Finally, each case of dementia is referred to the research unit to complete a full neuropsychological evaluation using the Uniform Data Set from the National Alzheimer's Coordinating Center's NACC (UDS, NB 3.0) that assesses episodic memory, processing speed, executive function, language, and construction capacity.²⁸ After a consensus meeting between neurologists, geriatricians, psychiatrists and neuropsychologists, the probable type of dementia was determined by applying the diagnostic criteria of DSM-5. This study included AD²⁹ and frontotemporal dementia (FTD).³⁰ Additionally, the Clinical Dementia Rating (CDR) scale³¹ was used for staging of dementia severity: CDR = 0 (control); CDR = 1 (mild dementia) and CDR = 2 (moderate dementia). CDR was administered to both participants and informants. The control group consisted of individuals who came to the clinic for cognitive complaints and had negative results on brief cognitive and functional tests.

Neurocognitive assessment

Mini-mental status examination (MMSE)

We used the Peruvian adaptation of the MMSE.³² MMSE is a classical instrument for assessing cognitive domains, such as verbal memory, working memory, language, and visuospatial functions.³² A score below 24 points has a sensitivity above 88.3% and a specificity close to 87% for detecting cognitive impairment in patients with dementia.²⁸

General anxiety disorder (GAD-7)

GAD-7 is one of the most frequently used diagnostic scales for screening, diagnosis and severity assessment of anxiety disorder.³³ The form was completed by study raters based on the participant's response. Most participants were able to complete the questionnaire in 5–10 min. GAD-7 total score for the seven items ranges from 0 to 21, since each of the 7 items can be scored from 0 (not at all) to 3 (nearly every day). Generalized anxiety was present if the total score is 10 or greater. As a severity measure, scores of 5, 10, and 15 represent cut points for mild, moderate, and severe anxiety, respectively.³³

Geriatric depression scale (GDS)

The GDS short form consists of 15 questions.³⁴ The questions used in this measure ask about common depressive symptoms that the participant has had during the past week. The form was completed by study raters based on the participant's responses. Most participants were able to complete the questionnaire in 5–7 min. Of the 15 items, 10 of the questions (numbers 2, 3, 4, 6, 8, 9, 10, 12, 14, 15) indicate the presence of depression when answered positively, while the rest (numbers 1, 5, 7, 11, 13) indicate depression when answered negatively. We count one point for every “yes” on questions 2, 3, 4, 6, 8, 9, 10, 12, 14, 15 and one point for every “no” on questions 1, 5, 7, 11, 13. Scores of 0–4 are considered normal, depending on age, education, and complaints; 5–8 indicate mild depression; 9–11 indicate moderate depression; and 12–15 indicate severe depression.³⁴

Neuropsychiatric inventory

NPI³⁵ was conducted via an interview with participant's caregivers. For each item, the caregiver is asked whether the participant has had any of the symptoms in the last month. If they respond no, they are scored a 0. If the caregiver responds yes, they are asked whether the severity is mild (1 point), moderate (2 points) or sever (3 points). The 13 questions ask about delusions, hallucinations, agitation/aggression, dysphoria/depression, anxiety, euphoria, apathy, disinhibition, irritability, aberrant motor behavior, sleep disturbances, and eating abnormalities.³⁵ The maximum possible score is 39, with higher scores indicating more severe behavioral symptoms.

Functionality assessment

The Pfeffer functional activities questionnaire

We administered the PFAQ to all caregivers. PFAQ is composed of 11 items assessing I-ADLs. Each item is rated on a 4- point scale from 0 (can complete without difficulty), 1 (can complete independently with difficulty), 2 (requires assistance), 3 (dependent). Total scores can range from 0 to 33 with higher scores indicating more functional impairment.¹²

Technology-activities of daily living questionnaire

We administered the PFAQ to all caregivers. T-ADLQ is composed of 33 items assembled into seven subscales (self-care activities, household care, employment and recreation, shopping and money, travel, communication, and technology) and three subdomains: B-ADLs, I-ADLs, and A-ADLs. Each question is rated on a 4-point scale from 0 (no difficulty performing the activity) to three (no longer capable of performing the activity). Furthermore, each item has the options for ‘never did this activity’(ND), ‘stopped the activity before the onset of dementia’ (‘S’), or ‘don’t know’ (DK). This allows for more nuanced responses to pre-dementia functioning.¹³

Statistical analysis

A nonparametric Kruskal-Wallis test was performed to compare demographic variables and A-IADL-Q-SV-P scores between the different levels of CDR. Subsequently, a quadratic discriminant classifier was trained to classify the CDR level, using 5-fold cross-validation. Sensitivity and specificity were determined from the confusion matrix. The area under the curve (AUC) was determined from the receiver operating curve. Spearman correlations were then performed between the A-IADL-Q-SV-P data of all participants and demographic variables (age, sex, and education) to assess relative associations. In addition, associations with existing cognitive (MMSE), mood (GAD-7, GDS, and NPI) and functionality (PFAQ and T-ADLQ) scales were analyzed. All analyses were performed in MATLAB, and data and code are available upon reasonable request to the corresponding author.

Standard protocol approvals, registrations, and participant consents

The study was approved by Committee for Medical and Health Research Ethics, Hospital Nacional Docente Madre-Niño- HONADOMANI “San Bartolomé” (12360–18). Written informed consent was obtained from all participants and caregivers enrolled in the study and was conducted in accordance with the Declaration of Helsinki.

Results

Subject demographics

Table 1 displays the demographic data for our cohort. We recruited 81 subjects with normal cognition (CDR = 0), 70 subjects with mild dementia (CDR = 1) and 20 subjects with severe dementia (CDR = 2). The sample was 75% female, which was balanced across CDR levels (p = 0.126). The average education level was 14 years indicating some post-secondary education, and years of education was also balanced across CDR levels (p = 0.448). The mild (CDR = 1) and moderate (CDR = 2) dementia groups also had similar percentages of participants with AD (∼56%, p = .303). The CDR = 1 group was older at 72 years compared to 68 years for the other two groups (p = 0.002). All clinical metrics were different between groups. Cognitive performance, measured by MMSE was significantly lower in CDR = 1 and CDR = 2 compared to CDR = 0. Functional impairment, measured by PFAQ and T-ADLQ increased progressively from CDR = 0 individuals to those with mild/moderate dementia. Mood assessments showed a similar trend, except GAD-7 followed a relatively linear progression from CDR = 0 to CDR = 1 to CDR = 2, with the difference from CDR = 0 to CDR = 1 similar to the difference from CDR = 1 to CDR = 2. For GDS, there was a jump of 2 points from CDR = 0 to CDR = 1, but no difference between CDR = 1 and CDR = 2. Notable, all groups had means in the normal range for GDS (score < 5).

Table 1.

Demographic data.

	CDR = 0	CDR = 1	CDR = 2	p
Demographic Data
N	81	70	20
Female: N (%)	61 (75%)	45 (64%)	17 (85%)	0.126
Alzheimer's disease: N (%)	–	44 (62%)	10 (50%)	0.303
Age	67.5 (6.6)	71.5 (6.6)	67.7 (9.8)	0.002
Education	14.0 (4.4)	13.6 (4.1)	14.4 (3.1)	0.448
Clinical Metrics
A-IADL-Q-SV-P	0.98 (3.19)	30.2 (20.3)	67.8 (15.9)	<0.001
Cognition
MMSE	27.8 (2.3)	22.6 (4.7)	17.0 (3.4)	<0.001
Mood
GAD7	1.8 (2.7)	3.8 (4.4)	5.1 (5.1)	<0.001
GDS	2.0 (1.9)	4.1 (3.3)	4 (2.6)	<0.001
NPI	1.1 (2.0)	5.4 (4.2)	12.1 (7.1)	<0.001
Functionality
PFAQ	0.06 (0.3)	9.4 (4.8)	21.7 (6.2)	<0.001
T-ADLQ	0.41 (2.5)	32.2 (17.9)	70.1 (15.0)	<0.001

Data is presented as mean (standard deviation), unless otherwise noted as number (%). p-value was calculated using Kruskal-Wallis non-parametric testing. A-IADL-Q-SV: Amsterdam Instrumental Activities of Daily Living Questionnaire-Short Version; GAD-7: Generalized Anxiety Disorder-7, GDS: Geriatric Depression Scale; MMSE: Mini-Mental State Examination; NPI: Neuropsychiatric Inventory; PFAQ: Pfeffer Functional Activities Questionnaire; T-ADLQ: Technology-Activities of Daily Living Questionnaire.

Discriminant validity of A-IADL-Q-SV-P for dementia severity

A-IADL-Q-SV-P effectively classified CDR level. We found the best cut-offs for CDR = 1 was 8, and for CDR = 2 was 59 (Figure 1(a)). The accuracy was 0.853 (Figure 1(b)) with an area under the curve (AUC) > 0.92 (Figure 1(c)). The A-IADL-Q-SV-P performed best at classifying normal cognition from dementia with a sensitivity of 0.95 and a specificity of 0.91 with an AUC of 0.89. Only 9 individuals with moderate dementia (13%) were classified as normal, and no individuals with moderate dementia were classified as normal. Only 3 controls (4%) were classified as mild dementia. These results were similar when investigating only the participants with either AD (accuracy > 0.88, sensitivity > 0.95) and or frontotemporal dementia (specificity > 0.88, AUC > 0.91, data not shown).

Figure 1.

Performance of CDR classification using A-IADL-Q-SV-P. (a) Raw scores with cutoffs defined by quadratic discriminant analysis. (b) Confusion matrix from the quadratic discriminant analysis. (c) Receiver operating curves (ROC) for Demographic data. Data is presented as mean (standard deviation), unless otherwise noted as number (%). p-value was calculated using Kruskal-Wallis non-parametric testing.

Association of A-IADL-Q-SV-P with other clinical markers

We found a correlation between A-IADL-Q-SV-P with age (rho = 0.198, p = 0.001, Figure 2(a)), but no correlation with years of education (rho = −0.112, p = 0.144, Figure 2(b)), and sex) (rho = −0.022, p = 0.778, Figure 2(c)). We also validated A-IADL-Q-SV-P, against other constructs of cognition, mood, and functionality (Table 2). The Spearman's correlation coefficient with MMSE was strong and negative (rho = −0.70, p < 0.001). There were moderate positive correlations with mood measures. The Spearman's correlation for GAD7 was 0.40 (p < 0.001), for GDS was 0.42 (p < 0.001), and for NPI was 0.63 (p < 0.001). As expected, other functionality scores had very high associations with the A-IADL-Q-SV-P. Correlation with PFAQ resulted in a Spearman's correlation coefficient of 0.87 (p < 0.001), and correlation with T-ADLQ resulted in a Spearman's correlation coefficient of 0.90 (p < 0.001).

Figure 2.

Association of A-IADL-Q-SV-P with demographic variables of (a) age, (b) education, and (c) sex. Statistics reported are Spearman correlations.

Table 2.

Construct validation with existing scales.

Measure	rho	p
Cognition
MMSE	−0.70	<0.001
Mood
GAD-7	0.40	<0.001
GDS	0.42	<0.001
NPI	0.63	<0.001
Functionality
PFAQ	0.87	<0.001
T-ADLQ	0.90	<0.001

Spearman's correlation coefficients are presented between A-IADL-Q-SV-P and scales of cognitive [Mini-Mental Status Exam (MMSE)], mood [Generalized Anxiety Disorder-7 (GAD-7), Geriatric Depression Scale (GDS), and Neuropsychiatric Inventory (NPI)], and functionality [Pfeffer Functional Activities Questionnaire (PFAQ) and Technology-Activities of Daily Living Questionnaire (T-ADLQ)].

Discussion

Our primary objective was to validate the robustness and diagnostic accuracy of A-IADL-Q-SV-P to distinguish healthy individuals from people with dementia in Lima, Peru. To our knowledge, this is the first study in a Spanish-speaking LA population to demonstrate the excellent psychometric properties of A-IADL-Q-SV-P to evaluate I-ADLs. The A-IADL-Q-SV-P effectively differentiated healthy controls from individuals with mild dementia, as well as mild from moderate dementia. The questionnaire was also significantly associated with other measures of cognitive and functional status. Importantly, we found A-IADL-Q-SV-P to not correlate with sex or education, which highlights the potential generalizability of this questionnaire. Although our sample did have an over-representation of women, this is consistent with the prevalence of dementia³⁶ and similar to community-based cohorts for other dementia studies in LA.^37–39 Nevertheless, sex was not correlated with A-IADL-Q-SV-P. The educational level for our sample was also similar to previous reports on functionality in Peru¹⁵ and LA.¹³ Peru is a multicultural country where disparities in education and regional differences in ADL can affect both assessment and diagnosis. Therefore, findings from Lima may not fully represent populations in rural or less-resourced areas. As for education, our cohort from people living in Lima had an average education level of 14 years. This indicated that many had post-secondary education. In Peru, 57% of individuals in Lima are able to reach secondary education, while only 12% of individuals in rural areas achieve secondary education.⁴⁰ There is also geographic difference in education obtainment where 39% of individuals in the mountains and 13% of individuals in the jungle reach secondary education.⁴⁰ Since we found years of education to not correlate with A-IADL-Q-SV-P, we can have some confidence in the validity across diverse groups, but future work should evaluate the questionnaire for individuals with very low educational levels.

In terms of discriminative validity, A-IADL-Q-SV-P demonstrated excellent diagnostic accuracy in classifying groups according to CDR results (accuracy: 0.853, AUC > 0.92). These results are consistent with a cross-sectional study that demonstrated that A-IADL-Q differed between participants with MCI and AD dementia.⁴¹ Additionally, it has previously been shown that patients with dementia showed a faster rate of decline according to I-ADL-Q scores compared to MCI patients and subjective memory complaints (SMC).²⁵ This individual variability is important because a portion of patients with MCI will convert to dementia over the course of the next few years. In that sense, patients with SMC and MCI who show a rapid rate of decline in I-ADL will be the patients who will convert to dementia in the future.

A-IADL-Q-SV-P showed strong correlation with cognitive assessment (measured by MMSE) and functionality (measured by PFAQ and T-ADLQ) demonstrating adequate construct validity. This is in agreement with a previous study that found high agreement with MMSE and Disability Assessment for Dementia.⁴² The British version (A-IADL-Q-UK) also showed a significant correlation with another measure of functionality, Measurement of Everyday Cognitive Function.²⁶ However, the association between the A-IADL-Q and cognitive assessment measures have shown mixed results. It is important to note that impairment of executive function is key to poor ADL performance.⁴³ Future studies with larger samples, including participants with SMC and MCI, and cognitive assessments that prioritize executive function will improve construct validity and investigate the ability of the A-IADL-Q-SV-P to predict real-world functioning.

Strengths and limitations

The strengths of this study lie in the importance of the adaptation and validation of a tool sensitive to early changes in functionality in a Latin American population that, according to our results, is not influenced by sex or years of education. In addition, A-IADL-Q-SV-P is easy to administer and is perceived by the informants as a user-friendly tool. The shorter form can produce results with the same quality as the longer version with fewer measurement errors and, therefore, is more reliable.⁴⁴ Another strength of the A-IADL-Q-SV-P is that it captures the perspective of caregivers, as they add a unique perspective in the daily functioning of individuals with dementia.^45,46 Finally, adaptation to the Peruvian context is important, because a mere translation of an instrument does not always represent cultural and ethno-racial disparities. We replaced some words that are not commonly used among older adults in Peru, which improved usability for the Peruvian public.

The limitations of our study include the small sample size, predominance of women, and recruitment in Lima, the capital of Peru, which limits the generalizability of our results. While conducting the research in a specialized dementia unit provided the capability to perform high-quality research in a LMIC, the cohort may not fully reflect the diverse population of the country. Of note, 30% of the population of Peru live in Lima. Nevertheless, future studies should expand our work to include a larger sample size representing a more diverse group of patient-caregiver pairs. Future studies should also investigate other cultural factors such as highest job title, socioeconomic status, and geographic location. In addition, we did not take into account some caregiver characteristics such as caregiver burden and depression that could alter the responses to the questionnaires related to I-ADL. However, low correlations have previously been shown between A-IADL-Q with caregiver burden and depression, which suggests these potential confounders would have little influence on our results. The most important limitation is the absence of subjects with MCI, so these populations should be considered in future studies. Longitudinal follow-up could also help determine whether A-IADL-Q-SV-P is an effective measure to monitor disease progression. Finally, in some regional economies such as Lima, A-IADL-Q-SV-P combined with biomarkers could be a defining diagnostic for better care.

Conclusion

In conclusion, the A-IADL-Q-SV-P demonstrated excellent accuracy in classifying dementia severity among older adults in Lima, Peru, with strong correlations to cognitive and functional measures, and minimal influence from demographic variables. The robust performance of the questionnaire across multiple dementia subtypes underscores its reliability and cross-cultural validity. Validation of this tool in a Spanish-speaking population within a low- and middle-income country is an important step to accurately classify functional capabilities in individuals, which is both clinically and scientifically valuable. The adaptability and diagnostic strength of the A-IADL-Q-SV-P support its integration into both research protocols and routine clinical assessments, contributing to improved identification and monitoring of functional decline in diverse aging populations.

Footnotes

Acknowledgements

The authors have no acknowledgments to report.

ORCID iDs

Gregory Brown

Belén Custodio

Diego Bustamante-Paytan

Nilton Custodio

Ethical considerations

The research activities involved in this study have been conducted in accordance with the ethical standards of the Helsinki Declaration. The study was approved by the Committee for medical and health research ethics, Hospital Nacional Docente Madre-Niño-HONADOMANI“San Bartolomé” (12360–18).

Consent to participate

All participants participated provided voluntarily in the study and provided written informed consent.

Consent for publication

All participants participated provided written consent to publish aggregated de-identified data.

Author contribution(s)

Rosa Montesinos: Conceptualization; Methodology; Project administration; Supervision; Writing – original draft.

Gregory Brown: Formal analysis; Investigation; Methodology; Visualization; Writing – original draft.

José Huilca: Investigation; Methodology; Writing – review & editing.

Belén Custodio: Conceptualization; Methodology; Project administration; Supervision; Writing – original draft.

Marco Malaga: Investigation; Methodology; Writing – review & editing.

Karol Lipa: Investigation; Methodology; Writing – review & editing.

Diego Bustamante-Paytan: Investigation; Methodology; Writing – review & editing.

Katherine Agüero: Data curation; Methodology; Project administration; Writing – review & editing.

Graciet Verastegui: Data curation; Methodology; Writing – review & editing.

Zadith Yauri: Data curation; Methodology; Writing – review & editing.

Nilton Custodio: Conceptualization; Methodology; Project administration; Supervision; Writing – original draft.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: There is no funding for this study. However, Rosa Montesinos and Nilton Custodio are partially supported by the following NIH grants: AG057234 and R56AG069118-01; and Alzheimer's Association: SG-21-715176-LATAM FINGERS and 24AARG-D-1246942. Gregory Brown was supported by the Fogarty International Center of the National Institutes of Health under Award Number D43TW009343 and the University of California Global Health Institute. No author has any relevant disclosure.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The deidentified data and MATLAB code associated with this study are available upon reasonable request to the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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