Quantitative Original Article

Pensar Enfermagem / v.30 n.01 / Jan-Dec 2026 / DOI: 10.71861/pensarenf.v30i1.509 / e00509

Development of a proposed scale for assessing fall risk in older

adults in primary care

Anabela Schinck1*, Edmundo Sousa2

1 Master. Research2Care. Local HealthUnit of Almada Seixal - USF Costa do Mar, Almada, Portugal; orcid.org/0000-

0001-5627-4674

2 PhD. School of Nursing, University of Lisbon, Lisbon, Portugal; orcid.org/0000-0003-2136-4471

* Corresponding author: anabelaslc@gmail.com

Received: 26.03.2026

Revised: 30.06.2026

Accepted: 25.10.2026

Editor: Florinda Galinha

How to cite this article: Schinck A, Sousa E. Development of a proposed scale for assessing fall risk in older adults in primary care. Pensar Enf [Internet].

2026; 30 (1): e00509. Available from: https://doi.org/10.71861/pensarenf.v30i1.509.

Abstract

Introduction

Population aging has been accompanied by an increase in falls among older adults, constituting an important

public health problem, particularly in primary care; however, fall risk assessment continues to rely on

instruments developed for hospital settings that are poorly sensitive to the specific features of

community settings.

Objective

To develop an easy-to-use scale for assessing fall risk in older adults in primary care, based on the

identification of clinical, sociodemographic, and psychosocial factors associated with previous falls.

Methods

An observational, analytical, cross-sectional, quantitative study was initially conducted with a convenience

sample of 322 older adults to identify factors associated with falls. Based on these results, a methodological

study was conducted, enabling the construction of a fall risk assessment scale using coefficients from a

logistic regression model.

Results

Inferential analysis identified significant associations between falls and sociodemographic, clinical, and

functional variables that, after multicollinearity analysis, were used to define and fit a logistic regression

model. The final model included four predictors - history of falls, age, fear of falling, and living

arrangement—whose coefficients were converted into point values to construct a proposed fall risk

assessment scale.

Conclusion

Fall risk in older adults is multifactorial, and the lack of instruments specifically designed for primary care

hinders its early identification. This study enabled the development of a fall risk assessment scale with

potential for use in identifying fall risk in older adults; however, evaluation in the development sample limits

the generalizability of the findings, and future external validation is needed.

Keywords

Aged, Accidental Falls, Risk Assessment, Primary Health Care, Frail Elderly.

Introduction

Population aging is one of the major demographic and public health challenges of the 21st century.

In Portugal, the aging index increased from 121.6 in 2010 to 182.7 in 2023, reflecting a growing proportion

of older adults in the population.1 This phenomenon is associated with a higher prevalence of chronic

Quantitative Original Article

Pensar Enfermagem / v.30 n.01 / Jan-Dec 2026 / DOI: 10.71861/pensarenf.v30i1.509 / e00509

diseases, functional dependence, and vulnerability, making older adults particularly susceptible to adverse

events such as falls.

Falls are among the leading causes of morbidity, loss of independence, and institutionalization in older

adults.2 An estimated 28% to 35% of individuals older than 65 years fall at least once a year, a proportion that

may reach 42% after age 75.3,4 In Portugal, data from the EVITA system show that, in 2023, more than 70%

of accidents involving older adults were falls, with 63.9% occurring at home.5 International studies support

these findings, particularly among individuals older than 75 years.6

Falls among older adults are multifactorial events with clinical, social, and economic implications. In addition

to physical injuries such as fractures, falls may trigger fear of falling, social isolation, depression, functional

decline, and increased health care costs.7 Fall prevention should therefore be a priority in health care,

especially in primary care, where monitoring and early intervention are essential. The clinical complexity of

the older adult population reinforces the need for a fall risk assessment instrument specifically tailored to the

primary care setting.8

Despite the high prevalence of falls, fall risk assessment in primary care continues to rely on instruments

developed for hospital settings, such as the Morse Fall Scale. This highlights a gap in clinical practice, since

risk factors in community settings span social, environmental, and behavioral dimensions that are not

captured by hospital-based scales.9,10

The Morse Fall Scale was developed by Janice Morse in 1985 and, in Portugal, was adapted and validated for

the adult population by Costa-Dias in 2014 and is recommended by the Directorate-General of Health for

fall risk assessment in hospital settings.11 It is, however, the scale used in primary care, despite not being

specific to this setting. Other scales have been developed to assess fall risk, but few have been validated for

use in primary care. The systematic review by Meekes, published in BMJ Open, analyzed six tools applicable in

primary care—including the Timed Up and Go Test (TUGT), Gait Speed, Berg Balance Scale, POMA, and

history of falls—and concluded that none showed robust predictive performance, with most studies

reporting AUC values between 0.5 and 0.74. Other scales, such as STRATIFY and FRAT, have been tested in

Portugal but have not demonstrated sufficient validity for systematic use in primary care.9,12,13 In Savić's study,

the Tinetti Scale indicated that most older adults over 75 years of age were at high fall risk6, highlighting the

greater vulnerability of this population and reinforcing the need for more sensitive tools adapted to

community settings.

The lack of a validated scale tailored to primary care justifies the development of a new instrument based on

data from noninstitutionalized older adults. This instrument incorporates risk factors identified in the

literature, including age over 65 years; sex; number of falls in the past 3 months; polypharmacy14,15; visual or

hearing impairment14; balance problems and other physical limitations15; and psychological factors such as

fear of falling.16 The availability of a simple, easy-to-use scale in primary care could help promote active

monitoring and the early implementation of preventive strategies.

The objective of this study is to develop an easy-to-use scale for assessing fall risk in older adults in primary

care, based on the identification of clinical, sociodemographic, and psychosocial factors associated with

previous falls.

Methods

A methodological study was conducted in two complementary stages. In the first stage, a cross-sectional

observational analysis of the data was performed following the STROBE recommendations to identify

factors associated with falls among older adults, a necessary step to support the development of the

instrument. In the second stage, the coefficients obtained from the logistic regression model were used to

develop a proposed fall risk assessment scale by assigning weights to the significant variables.

The discriminatory ability of the scale was subsequently evaluated in the same sample using a receiver

operating characteristic (ROC) curve.

Data were collected in 2019 at a Family Health Unit (FHU) in the Almada region. The study population

included all individuals aged 65 years or older registered with the FHU, and the sample was selected by

convenience using nonprobability sampling. All older adults who voluntarily agreed to participate during the

study period, either during an in-person consultation or a home visit, were included. Inability to sign the

informed consent form was considered an exclusion criterion.

A total of 322 valid questionnaires were collected from participants who met the inclusion criteria and agreed

to participate in data collection. This sample size was considered adequate for the statistical analysis

performed.17,18

The following variables were considered: a) sociodemographic variables (sex, age, educational attainment,

housing type, household composition, and marital status); b) clinical and functional variables (history of falls,

Quantitative Original Article

Pensar Enfermagem / v.30 n.01 / Jan-Dec 2026 / DOI: 10.71861/pensarenf.v30i1.509 / e00509

comorbidities, polypharmacy, fear of falling, balance, mobility, and Barthel Index); and fall risk (assessed

using the Morse Fall Scale).9,19

The self-administered questionnaire comprised four sections: 1) sociodemographic characteristics; 2) clinical

and functional variables; 3) Barthel Index; and 4) Morse Fall Scale. For participants who had difficulty reading

or writing, the questionnaire was administered with the assistance of a family member or a health care

professional, who read the questions and recorded the responses provided by the older adult.

Based on the factors identified and their clinical relevance, a fall risk assessment scale was constructed, with a

score assigned to each variable according to its relative weight in relation to the outcome.

For model construction, the variables were recorded as dichotomous. Age was recorded as <80 vs ≥80 years,

a cutoff supported by the literature indicating a significantly increased risk of falls among older adults aged

80 years or older. At this stage, it is important to distinguish the dependent variable (“falls in the past 3

months”) from the predictor variable “history of falls.” The outcome was recoded as 0 = up to one fall and 1

= two or more falls, whereas “history of falls” referred to previous falls and was entered into the model as an

independent predictor.

Data was analyzed using IBM SPSS Statistics® 13.0. Descriptive statistics were initially used to characterize

the sample, and inferential statistics were used to identify factors associated with fall risk using binary logistic

regression and corresponding 95% confidence intervals.

The study was approved by the Ethics Committee of the Regional Health Administration of Lisbon and

Tagus Valley (ARSLVT) (Opinion No. 074/CES/INV/2019). All participants were informed about the

objectives, risks, and benefits of participation and signed the informed consent form. Anonymity and

confidentiality were ensured, and participants could withdraw at any time without affecting the care they

received. The scales used had previously been validated for the Portuguese population, and permission to use

them was requested from the respective authors.

Results

The sample consisted of 322 older adults with a mean age of 78.25 years (minimum 65, maximum 99). The

remaining sociodemographic characteristics are shown in Table 1.

Table 1. Sociodemographic characteristics of the sample.

Variable

Category

Final score

History of falls

Yes

Age

≥ 80 years

< 80 years

Fear of falling

Yes

Living arrangement

Alone or with +2 people

With 1 person

Because the “history of falls” variable had a larger coefficient than the other variables, the coefficients were

normalized to facilitate clinical application and assessment of the scale. Point assignment was intended to

preserve the order of importance of the predictors identified by the model while preventing a single factor

from disproportionately determining the overall risk classification.

The 6-point score assigned to history of falls was not mathematically derived (statistical score) but reflected a

clinical decision (clinical score). The intention was to reflect the clinical relevance of this factor as one of the

main predictors of subsequent falls without making the scale dichotomous or allowing this variable to nullify

the contribution of the remaining predictors. A weight of 32 points would mean that the presence of a

history of falls would, by itself, determine nearly the entire risk classification, substantially reducing the

contribution of the remaining variables to discrimination.

Thus, this variable was retained as the factor with the greatest weight in the scale, reflecting its recognized

clinical relevance, while a weighting scheme was adopted to incorporate all predictors in a balanced manner.

This adaptation therefore represents a decision based on clinical criteria and should be reevaluated in future

validation studies.

Quantitative Original Article

Pensar Enfermagem / v.30 n.01 / Jan-Dec 2026 / DOI: 10.71861/pensarenf.v30i1.509 / e00509

Figure 1. ROC curve for the final model

The logistic regression model had an area under the ROC curve (AUC) of 0.989 and showed high

discrimination in the study sample, suggesting that it may distinguish individuals at risk of falls from those

not at risk.

Based on the predictors identified by the model, a proposed scale was constructed for future clinical

application and validation.

Discussion

Fall risk is a multifactorial phenomenon influenced by clinical, functional, psychological, and social

characteristics13. The lack of a specific instrument for identifying older adults at higher fall risk poses a

relevant challenge in health care, since existing instruments are not adapted to the primary care setting.12,13

In the sample of 322 older adults, 26.1% had experienced at least one fall in the previous 3 months, a finding

consistent with the annual prevalence of falls reported in community-based studies, which ranges from 26%

to 35%6,22. Falls were significantly associated with sociodemographic and clinical variables. Despite the

multifactorial nature of falls, these findings enabled the identification of a set of variables independently

associated with higher fall risk, particularly comorbidities and functional and psychological factors.6,8

The need for a multifactorial scale has also been suggested by other authors4, 12, 13, who refer to the use of

“physical tests” within an approach that combines fall history with other domains.

The proposed scale includes “history of falls,” the variable with the greatest weight, a finding consistent with

evidence that a previous fall is one of the strongest individual predictors of future falls6, 8, 23; “age,” a robust

predictor after age 65, with risk increasing further after age 802, 6, 8, 24; “fear of falling,” recognized as a

determinant of fall risk16, 25, 26 and selected to combine physical and psychological factors16, 27; and “living

arrangement,” a key environmental variable in primary care.12, 13 Here, the “history of falls” item corresponds

to the predictor identified in the model (“two or more falls”) and was simplified to “Yes/No” to facilitate

clinical use.

After the independent predictors were identified, the proposed scale was constructed. The scoring was

adapted to facilitate clinical use of the scale while preserving the hierarchy of the predictors identified by the

model. The reduction in the weight assigned to history of falls reflected a clinical decision intended to

prevent a single predictor from dominating the overall risk classification. Risk categories should subsequently

be established and validated in future studies.

In the present sample, the logistic regression model showed high discrimination (AUC = 0,989), a value

higher than those usually reported, as moderate AUC values are more common.4, 12, 13 However, this value

should be interpreted with caution because it may reflect overfitting to the dataset, as the model was

Quantitative Original Article

Pensar Enfermagem / v.30 n.01 / Jan-Dec 2026 / DOI: 10.71861/pensarenf.v30i1.509 / e00509

developed and tested in the same sample. In addition, the presence of a predictor with a very high weight in

the final scale score (history of falls) may influence the outcome, contributing to such an optimistic AUC

value, particularly because the scale was tested in the same sample in which it was developed; subsequent

external validation is therefore needed. Similar studies generally report lower values.4,12

The study has limitations that should be considered: convenience sampling and data collection at a single

health care unit, which may limit the generalizability of the findings because the sample may not be

representative of the older adult population in primary care; lack of longitudinal follow-up; and low internal

consistency, consistent with the multidimensional nature of falls. Given the sequence of procedures used and

because the proposed scale was evaluated in the same sample used to construct it, the results reflect only

internal validity and possible overfitting, rather than future predictive ability. Furthermore, the high weight of

history of falls may have influenced the observed discriminatory ability, an aspect that should be reevaluated

in future studies.

The study findings support the proposed scale as a potential instrument for assessing fall risk among older

adults in primary care; however, external validation is still needed. Its use in future studies conducted in

primary care is recommended to assess its predictive ability at 3, 6, and 12 months.

Conclusion

In this study, several factors associated with falls among older adults were identified, enabling the

development of a proposed fall risk assessment scale designed for primary care: history of falls, age, fear of

falling, and living arrangement.

The use of a limited set of variables significantly associated with fall risk allows the scale to be completed and

administered quickly, which may encourage its use by health care professionals and make it a useful tool in

primary care. Although promising, the scale was evaluated in the same sample used to develop it, limiting the

generalizability of the findings.

Nevertheless, the study makes relevant contributions. For research, it represents a first step toward

developing a multifactorial scale specifically designed for primary care, which should be further tested.

For clinical practice, the proposed scale may support the early identification of older adults at risk and guide

more targeted preventive interventions. In the area of education, the findings reinforce the need to train

health care professionals to assess fall risk systematically and comprehensively, taking physical, psychological,

and social dimensions into account and promoting a broader preventive approach.

Authorship and Contributions

AS: Conception/design, data collection, analysis and interpretation, writing the manuscript, critical revision;

approval of the final version, taking responsibility for it.

ES: Conception/design, writing the manuscript, critical revision; approval of the final version of the

manuscript and taking responsibility for it.

Conflicts of interest and Funding

No conflict of interest has been declared by the authors.

Acknowledgments

We acknowledge the contribution of nurse Ana Patrícia Martins.

Sources of support / Financing

The study was not funded.

Data availability statement

The dataset supporting the findings of this study is available from the authors upon reasonable request.

Access may be provided for academic or research purposes, subject to appropriate ethical and confidentiality

considerations.

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