To our knowledge, this is the first study to propose a cut-off value for classifying physical inactivity in older adults using the PASE. The study was designed to determine the cut-off value of the PASE for physical inactivity in older adults. The results of the present study demonstrated that the PASE can assess physical inactivity in older adults and that people with a score below 67 are physically inactive. For identifying physical inactivity, a PASE score ≤ 67 has a sensitivity of 0.76 and a specificity of 0.61 (moderate discrimination). Although the PASE showed moderate discrimination, the relatively low specificity suggests a risk of misclassifying physically active individuals as inactive. This may limit its utility for screening at the individual level, though it remains useful for population-level monitoring. Consequently, its utilization as a stand-alone measure for this purpose is not recommended. Instead, its application should be integrated with diverse subjective and objective physical activity assessment techniques.
There are studies in the literature that assess the prevalence of physical inactivity via a variety of assessment methods in different population groups [7,8,9]. Studies investigating the prevalence of physical inactivity in older adults in different countries have revealed that 29.8% of those in Malaysia [7], 36.7% of those in India [8], 50.7% of those in Brazil [9] were physically inactive. In this study, the prevalence of physical inactivity was 41.37%. It has been suggested in the literature that there may be many reasons for differences in prevalence between populations. Factors such as different characteristics of the populations (developing vs. developed society, physical activity intervention plans of countries, etc.), the time when the assessments were made (pandemic period, etc.), the methods used in the assessment have different cut-off values, and the age distribution of the individuals participating in the study may cause prevalence rates to vary [21].
In addition to accurate and reliable assessments of physical inactivity in older adults, the establishment of reference values is highly important. A review of the literature examined the cut-off values of both subjective and objective measures of physical activity in relation to physical inactivity [10]. The review identified cut-off values for physical inactivity of 7500 steps/day for pedometers [22], less than 150 min of moderate-to-vigorous physical activity (MVPA) per week for the IPAQ [23], and 600 MET minutes/week of MVPA for the GPAQ [24]. In this study, the PASE cut-off for physical inactivity was 67. The identified cut-off of 67 on the PASE is lower than the activity thresholds of 600 MET-min/week (GPAQ) or 150 min of MVPA per week (IPAQ), suggesting that the PASE may be more sensitive to low-level activity common among older adults.
In this study, we wanted to classify older adults as active/inactive on the basis of the PASE assessment, similar to the IPAQ questionnaire. Therefore, we interpreted the PASE data via the IPAQ questionnaire. The IPAQ was selected as the reference due to its widespread international use, validated cut-off points, and previously reported moderate-to-high correlation with the PASE in older adult populations [16].
The large number of physical activity questionnaires makes it difficult to select the questionnaire with the best measurement properties. The use of different questionnaires in the literature reduces the comparability of physical activity estimates between studies and their associations with health outcomes. To limit methodological bias and produce study results of the highest quality, the selection of a questionnaire with the optimal set of measurement properties for a particular purpose is highly important [25]. A review of the assessment characteristics of physical activity questionnaires used in older adults recommended the PASE for total physical activity and the Physical Activity and Sedentary Behavior Questionnaire (PASB-Q) for MVPA [25]. Therefore, we believe it is valuable to classify older adults as inactive or active according to the PASE.
The strength of this study is that we used the IPAQ Short Form, which was developed as a standardized method for assessing the prevalence of physical inactivity and allows international researchers to assess global levels of physical inactivity, and the PASE, a physical activity questionnaire specifically for older adults. Importantly, PASE has been designed with elderly people in mind and addresses home-based activities. In addition, the large sample size increases the power of this study.
The cross-sectional design limits causal inference, and the use of a single-center sample may reduce the generalizability of findings. Furthermore, the lack of objective measures such as accelerometry limits the validation of self-reported data. In future studies, new technologies and device-based measures such as pedometers and other electronic devices that measure physical activity can be used in combination with the PASE to assess the prevalence of physical inactivity accurately. Furthermore, it is important to acknowledge that the outcomes of physical activity assessments may be influenced by the distinct characteristics of the studied populations and the timing of the assessments.
Another limitation of the present study is the lack of internal validation (e.g., bootstrapping or use of an independent validation sample) for the sensitivity and specificity values obtained from the ROC curve analysis. Although the relatively large sample size may partially mitigate this concern, future studies are encouraged to employ resampling techniques to enhance the robustness, generalizability, and reproducibility of discriminative thresholds.
In addition to trying to keep older adults physically active, there are now recommendations in the literature to sit less during the day and reduce the continuity of sitting time by making frequent breaks while sitting [26]. Therefore, we believe that it will be important for future studies to evaluate in detail not only the concept of physical inactivity but also the concept of sedentary behavior in older adults and to establish cut-off values for sedentary behavior. Future longitudinal studies integrating device-based measures such as accelerometers or smart wearables are warranted to validate the proposed PASE cut-off. Additionally, research should explore combined models that include sedentary behavior metrics to improve overall health risk prediction in older adults.