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. 2022 Dec 14;19(24):16776. doi: 10.3390/ijerph192416776

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© 2022 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/).

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Binary tree computed by conditional recursive partitioning on frailty classes, global model. Effect of SF-36-PCS (the physical indicator of health-related quality of life), SF-36-MCS (the mental indicator of the quality of life), GDS-15 (0–5 = no depression, 6–9 = mild depression, 10–13 = depression), MMSE, together with sex, age groups, BMI classes, number of daily drugs (“Drugs”), absence or presence of caregiver (NO/YES), annual income, absence or presence of leisure activities (NO/YES), and level of education (School), on frailty classes (0 = robust, 1 = pre-frail, 2 = frail subjects). The variables affecting frailty are SF-36-PCS, having or not having a caregiver, GDS-15, SF-36-MCS, leisure activities, and education.