Frailty, Seasonal Sensitivity and Health-related Quality of Life in Older People Living in High Southern Latitudes: a Bayesian Analysis

Abstract

Background
In older people, a notable research gap exists regarding the intricate dynamics between frailty, seasonal sensitivity, and health-related quality of life (HRQoL). This study aimed to determine the association between frailty, seasonal sensitivity, and HRQoL in older people from high southern latitudes.

Methods
A cross-sectional observational study was conducted. Frailty, seasonal sensitivity, and HRQoL measurements were self-reported by participants through questionnaires. A total of 118 older people were recruited from a local community. The participants were selected through intentional non-probabilistic sampling.

Results
The adjusted models showed a trend where lower education was associated with a higher risk of frailty (BF = 0.218). For frailty and HRQoL, we observed a trend suggesting that HRQoL decreases with increasing severity of frailty (BF = 1.76). In addition, we observed a linear effect based on the severity of seasonal sensitivity, meaning that older people with higher perceived severity report a proportional decrease in HRQoL (BF = 6.66).

Conclusion
Sociodemographic factors, such as lower education levels, have increased the risk of frailty. At the same time, frailty and seasonal sensitivity perceived severity were associated with a lower HRQoL in older people.

Full text

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ABSTRACT
Background
In older people, a notable research gap exists regarding the
intricate dynamics between frailty, seasonal sensitivity, and
health-related quality of life (HRQoL). This study aimed to
determine the association between frailty, seasonal sensitiv-
ity, and HRQoL in older people from high southern latitudes.
Methods
A cross-sectional observational study was conducted. Frailty,
seasonal sensitivity, and HRQoL measurements were self-
reported by participants through questionnaires. A total of
118 older people were recruited from a local community.
The participants were selected through intentional non-
probabilistic sampling.
Results
The adjusted models showed a trend where lower education
was associated with a higher risk of frailty (BF = 0.218).
For frailty and HRQoL, we observed a trend suggesting that
HRQoL decreases with increasing severity of frailty (BF =
1.76). In addition, we observed a linear effect based on the
severity of seasonal sensitivity, meaning that older people
with higher perceived severity report a proportional decrease
in HRQoL (BF = 6.66).
Conclusion
Sociodemographic factors, such as lower education levels,
have increased the risk of frailty. At the same time, frailty and
seasonal sensitivity perceived severity were associated with
a lower HRQoL in older people.
Key words: older adults, frail elderly, aged, seasonal affective
disorder, quality of life, Bayesian analysis
INTRODUCTION
The global demographic transition towards an aging popula-
tion presents signicant challenges in addressing the health
and well-being of older people.(1,2) As older people face unique
health vulnerabilities and complexities, understanding the
factors inuencing their overall health-related quality of life
(HRQoL) becomes paramount.
Frailty is a geriatric syndrome that refers to a dynamic
state of increased vulnerability and decreased physiological
reserve, often observed in older people.(3) Frail people are at
higher risk of adverse health outcomes, reduced functional
capacity, and impaired quality of life.(3-8) This state of vulner-
ability, however, is not solely shaped by individual physio-
logical factors; environmental inuences play a signicant
role in the manifestation of frailty.(9) As we delve into the
complex interplay between health and the environment, an
emerging area of research has garnered increasing attention
in recent years: seasonal sensitivity.(9)
Seasonal sensitivity, characterized by individual varia-
tions in response to seasonal changes, has a pronounced effect
in high-latitude regions, impacting older people’s physical
and psychological well-being.(10,11) In the high-latitude areas,
pronounced seasonal variations can impact older residents’
physical and psychological well-being.(11–15) Furthermore,
seasonality—the predictable changes in the environment and
climate—could surpass the adaptability of frail people, lead-
ing to disability and vulnerability with a seasonal pattern.(9)
Frailty, Seasonal Sensitivity and Health-related
Quality of Life in Older People Living in High
Southern Latitudes: a Bayesian Analysis
Diego Mabe-Castro, Student1,2, Karen Tobar Gomez, Graduate1, Matías Castillo-Aguilar, Graduate1,2,
Sebastián Jannas-Vela, PhD3,4, Eduardo Guzmán-Muñoz, PhD5,6, Pablo Valdés-Badilla, PhD7,8,
Cristian Núñez-Espinosa, PhD1,4,9
1Centro Asistencial Docente y de Investigación, Universidad de Magallanes, Punta Arenas; 2Kinesiology Department, University
of Magallanes, Punta Arenas; 3Instituto de Ciencias de la Salud, Unviersidad de O’Higgins, Rancagua; 4Interuniversity Center
for Healthy Aging RED21993, Talca; 5Escuela de Kinesiología, Facultad de Salud, Universidad Santo Tomás, Santiago; 6Escuela
de Kinesiología, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca; 7Department of Physical Activity
Sciences, Faculty of Education Sciences, Universidad Católica del Maule, Talca; 8Carrera de Entrenador Deportivo, Escuela de
Educación, Universidad Viña del Mar, Valparaiso; 9Escuela de Medicina, Universidad de Magallanes, Punta Arenas, Chile
https://doi.org/10.5770/cgj.27.719
ORIGINAL RESEARCH
© 2024 Author (s). Published by the C anadian Geriatrics Society. This is an Open Access ar ticle distributed under the terms of the Cr eative Commons Attribution Non- Commercia l
No-De rivative license (https://creativecommons.org/licenses/by-nc-nd/4.0/), which per mits unrestric ted non-commerc ial use and distribu tion, provided the origi nal work is properly cited.

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Hence, investigating the association between frailty and sea-
sonal sensitivity in this population is critical for enhancing
their health outcomes and HRQoL.
HRQoL is often one of the most important outcomes
targeted by interventions and public policies. It is a multi-
dimensional construct encompassing various aspects of
an individual’s physical health, psychological well-being,
social interactions, and environmental factors.(16) As a com-
prehensive measure of an individual’s life satisfaction and
functioning, assessing HRQoL is fundamental, especially in
older people.(17)
Despite the growing interest in the frailty, seasonal
sensitivity, and HRQoL aspects, the evidence in this regard
is limited, and these variables have been scarcely explored,
especially in older people living in extreme environments.
This research aims to determine the association between
frailty, seasonal sensitivity, and HRQoL in older people
from high southern latitudes. We hypothesize that frailty is
related to seasonal changes and is associated with decreased
health-related quality of life.
METHODS
Study Design
An observational cross-sectional and correlational study was
conducted. The study was informed to all participants before
data collection. Sociodemographic information, seasonal
sensitivity, quality of life, and frailty data were obtained
through self-report measures, while morphological meas-
urements were obtained through bioimpedance and manual
height rods.
Participants were informed about the objectives and
assessments of this study, and they voluntarily signed the
informed consent form. The assessments consisted of admin-
istering the criteria of the FRAIL scale for frailty, the SPAQ
for seasonal sensitivity, and the WHOQoL-Old questionnaire
for HRQoL. The questionnaires were administered privately
by trained collaborators to address any questions or doubts,
ensuring the acquisition of reliable responses.
Participants
One hundred eighteen participants were recruited from a
local community in the Magallanes and Chilean Antarctic
Region, Chile (located at high southern latitudes 48°36’ to
56°30’). They were included in the analysis if they: (a) per-
manently resided in the Magallanes region, (b) were 60 years
or older, and (c) were sufciently autonomous to respond
to self-reported questionnaires. However, participants were
excluded if they: (a) had severe cognitive impairments or
dementia that could hinder their ability to provide reliable
self-reported data, (b) had signicant communication barriers
that prevented them from completing the self-report question-
naires effectively, or (c) were currently undergoing primary
medical treatments or facing severe health conditions that
could confound the study results.
Ethics
All participants gave their permission and signed informed
consent before participation. The Ethics Committee of the
University of Magallanes (N°008/SH/2022) approved this
study following the regulations established by the Declaration
of Helsinki on ethical principles in human beings.
Measures
Before the evaluations, the participants’ sociodemographic
data was collected, such as name, age, marital status, educa-
tional level, and chronic diseases.
Frailty
The Spanish version of the FRAIL scale was employed.(18)
It encompasses fatigue, ambulation, resistance, illness, and
weight loss criteria. One point is attributed to each domain
and the scale scores from 0 to 5 points (0 = best to 5 = worst).
Individuals are categorized as non-frail (0 points), pre-frail
(1 or 2 points), or frail (3 or more points).(19) The question-
naire is an optimal screening test for clinicians to identify frail
persons at risk of declining health and mortality.(20)
Seasonal Pattern
Spanish language adaptation of the Seasonal Pattern Assess-
ment Questionnaire (SPAQ) in the adult versions.(21) The
SPAQ measures seasonality classied as changes in mood
and behavior through the seasons, treated as a cyclical pattern
of depressive episodes with criteria of major depression that
appear in the autumn–winter period and tend to present with
atypical symptoms, such as hypersomnia, hyperphagia, and
appetite for carbohydrates. The Spanish version of the SPAQ
gives adequate reliability and internal consistency values for
its use in epidemiological and clinical research.(21)
Health-related Quality of Life (HRQoL)
HRQoL was measured using the WHOQoL-Old instrument,
validated in the Chilean older people. It assesses six dimen-
sions (sensory abilities, autonomy, past, present, and future
activities, social participation, death and dying, and intimacy)
with 24 items on a 5-point Likert scale. The instrument has
demonstrated good internal consistency in the Chilean popula-
tion with a Cronbach’s alpha of 0.83.(22)
Statistical Analysis
A total of 118 participants were included. In this data set, we
aimed to explore the relationship between seasonal sensitivity,
HRQoL, and frailty using a model-based inference approach
under a Bayesian framework to describe the uncertainty
associated with model parameters.
In this context, we developed three models to delve
into different aspects. Firstly, we examined how the level of
education impacts frailty scores. Secondly, we delved into
the inuence of fragility classication on HRQoL scores.
Finally, we aimed to unify the preceding models by investi-
gating the connection between perceived seasonal sensibility
severity and HRQoL scores. To achieve this, we employed
non-orthogonal polynomial contrasts up to the third order,

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allowing us to explore potential linear, quadratic, and cubic
trends associated with seasonal sensitivity.
As part of our strategy was to enhance model robustness
and to mitigate the impact of outliers during the model tting
process, we opted for weakly informative priors for our model
parameters. These priors are detailed in Equation 1, ensuring
a balanced and practical approach.
β ~ Normal (0,10) (1)
σ ~ HalfCauchy (0,15)
Following the Sequential Effect eXistence and sIgni-
cance Testing (SEXIT) framework,(23) we report the median of
the posterior distribution along with its 95% credible interval
(CI), the probability of direction (pd), the probability of sig-
nicance, and the probability of being large. The thresholds
used to determine signicance (i.e., non-insignicant) and
largeness were |0.05| and |0.30| of the standard deviation of
the response variable, respectively.
Additionally, we present the Bayes factor (BF10), which
indicates the degree to which the posterior distribution has
shifted away from or towards the null value or values (rela-
tive to the prior distribution). This provides information on
whether the null value has become more or less likely given
the observed data. A Bayes factor greater than 1 can be
interpreted as evidence against the null model, and a conven-
tion is that a Bayes factor greater than 3 can be considered
“substantial” evidence against the null model (conversely, a
Bayes factor less than 1/3 indicates substantial evidence in
favor of the null model). All computations were performed
using the R programming language for statistical computing
on version 4.2.1 (R Foundation for Statistical Computing;
https://www.r-project.org/foundation/).
RESULTS
Sample Characteristics
A total of 118 participants (male, n = 28 [23.7%]; female, n =
90 [76.3%]) were enrolled in the study. Sample characteristics
and body composition parameters can be observed in Table 1.
Education Level and Risk of Frailty
The adjusted models show a trend suggesting that a lower
education level is associated with a higher risk of frailty. When
examining pairwise differences, older people with a middle
TABLE 1.
Overall descriptive statistics were grouped by maximum educational level reached
Educational Level
Characteristic Overall, N = 118aPrimary, N = 41aSecondary, N = 56aHigher, N = 21a
Age (years old) 70.5 (5.9) 72.5 (6.0) 69.6 (5.8) 68.7 (5.2)
Frailty score 0.55 (0.76) 0.78 (0.88) 0.46 (0.69) 0.33 (0.58)
Frailty Classication
Non-frail 68 (58%) 19 (46%) 34 (61%) 15 (71%)
Pre-frail 48 (41%) 20 (49%) 22 (39%) 6 (29%)
Frail 2 (1.7%) 2 (4.9%) 0 (0%) 0 (0%)
Self-Perceived Seasonality Severity
Not a problem 87 (74%) 33 (80%) 39 (70%) 15 (71%)
Mild 9 (7.6%) 1 (2.4%) 4 (7.1%) 4 (19%)
Moderate 8 (6.8%) 2 (4.9%) 5 (8.9%) 1 (4.8%)
Important 7 (5.9%) 4 (9.8%) 3 (5.4%) 0 (0%)
Severe 6 (5.1%) 1 (2.4%) 4 (7.1%) 1 (4.8%)
Serious 1 (0.8%) 0 (0%) 1 (1.8%) 0 (0%)
Seasonal Sensitivity Index
Typical 68 (67%) 22 (65%) 36 (73%) 10 (56%)
Winter blues 22 (22%) 8 (24%) 8 (16%) 6 (33%)
SAD 11 (11%) 4 (12%) 5 (10%) 2 (11%)
Unknown 17 7 7 3
HRQoL Overall Score 101 (14) 101 (15) 100 (14) 102 (13)
Marital Status
Married 71 (60%) 22 (54%) 36 (64%) 13 (62%)
Divorced 7 (5.9%) 2 (4.9%) 0 (0%) 5 (24%)
In a relationship 1 (0.8%) 1 (2.4%) 0 (0%) 0 (0%)
Separated 6 (5.1%) 0 (0%) 5 (8.9%) 1 (4.8%)
Single 9 (7.6%) 5 (12%) 3 (5.4%) 1 (4.8%)
Widow 24 (20%) 11 (27%) 12 (21%) 1 (4.8%)
aMean (SD); n (%)

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education level have 0.32 points less (95% CI [-0.62, -0.02])
than those with only primary education (pd = 98%, Signi-
cant = 96%, Large effect = 72%, BF = 0.107). Furthermore,
older people with higher education levels have 0.45 points
less (95% CI [-0.84, -0.05]) than those with lower education
levels (pd = 99%, Signicant = 98%, Large effect = 86%,
BF = 0.218) (see Figure 1).
Frailty and Overall HRQoL
For frailty and HRQoL, we observed a trend suggesting that
HRQoL decreases with severity of frailty. When comparing
with “Non-frail” older people, those classied as “Pre-Frail”
had 5.83 points less (95% CI [-10.77, -0.86]) in terms of
HRQoL (pd = 99%, Signicant = 98%, Large effect = 73%,
BF = 3.22). Meanwhile, participants classied as “Frail”
had 9.31 points less (95% CI [-23.08, 4.64]) in HRQoL
(pd = 91%, Signicant = 88%, Large effect = 76%, BF = 1.76)
(see Figure 2).
When examining the relationship between both scores,
HRQoL and frailty, we observed a decrease of 4.82 points
(95% CI [-8.08, -1.56]) (pd = 100%, Signicant = 99%, Large
effect = 63%, BF = 6.18).
Perceived Severity of Seasonal Sensitivity
and HRQoL
Upon examining the effect of seasonality on HRQoL, we
observed a linear effect based on the severity of seasonal
sensitivity, suggesting that with higher perceived severity,
there is a proportional decrease in HRQoL. Thus, for each
movement from a lower severity category to a higher severity
one, we observed a proportional decline of 12.36 points (95%
CI [-23.49, -1.17]) in HRQoL (pd = 98%, Signicant = 98%,
Large effect = 92%, BF = 6.66) (see Figure 3).
DISCUSSION
This study aimed to determine the association between
frailty, seasonal sensitivity, and HRQoL in older people high
southern latitudes. Utilizing Bayesian statistics, we explored
the uncertainties associated with these relationships and their
implications for the well-being of older populations in regions
with pronounced seasonal variations. Therefore, the hypoth-
eses were conrmed. Our ndings offer valuable insights by
addressing a prior knowledge gap, as we, for the rst time,
delve into the interactions between frailty, seasonal sensitiv-
ity, and HRQoL in older people residing in the high southern
latitudes. This research pioneers in an environment where
the relationship between these factors is largely uncharted.
Furthermore, our study employed a Bayesian statistical
approach, introducing an innovative dimension to the analysis
and comprehension of these relationships, thus contributing
to the advancement of knowledge in this specic eld.
The signicant association between education level and
frailty risk aligns with existing literature that highlights the
impact of socioeconomic factors on health status in older
people, especially in low-income countries.(24–26) Even more,
FIGURE 1. Posterior distributions of plausible true values (φ for frailty score at each educational level

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FIGURE 2. Posterior distributions of plausible φ for quality of life at each fragility classication
FIGURE 3. Posterior distributions of plausible φ for quality of life for each self-reported seasonality severity; parameter
estimation is based on polynomial contrasts of the third order

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CANADIAN GERIATRICS JOURNAL, VOLUME 27, ISSUE 1, MARCH 2024
a lower education level has been previously associated with
lower physical performance(27) and higher odds of being
frail.(28) These results emphasize the need to address educa-
tional disparities to enhance older adults’ health and functional
capacity in high-latitude regions.
The observed substantial negative association between
frailty severity and HRQoL reinforces the importance of early
detection and targeted interventions to mitigate the impact of
frailty on HRQoL in older people. These ndings resonate
with existing literature on the adverse effects of frailty on
various aspects of well-being.(29–31) Furthermore, the linear
effect of seasonal sensitivity severity on HRQoL highlights
the importance of addressing the impact of seasonal changes
in health-care interventions and public health policies. A
previous study conducted in extreme environments concluded
that seasonal sensitivity affects psychological well-being in
older people;(11) however, specic HRQoL assessing instru-
ments were not employed. This observation contributes to
the limited research on seasonal variations’ and perceived
severity effects on HRQoL in older people, particularly in
high-latitude regions.
Even more, the Bayesian perspective employed in this
study adds rigor to interpreting the observed associations by
accounting for uncertainties and enhancing the reliability of
our conclusions.(32-34) Using Bayesian statistics in the con-
text of aging research and seasonal variations offers a novel
approach, enriching the understanding of the complex rela-
tionship regarding frailty, seasonal sensibility, and HRQoL.
The implications of these ndings are signicant for
health-care policy and interventions. Addressing educational
disparities and providing targeted support to older adults with
lower education levels can enhance resilience and reduce
frailty risk.(26-28) Early detection and interventions for frailty
can improve overall HRQoL in older populations.(30-31) Recog-
nizing the impact of seasonal changes on HRQoL can inform
targeted interventions to support older people in regions with
extreme seasonal variations.(11) Also, this study provides
valuable insights into the associations between frailty, sea-
sonal sensitivity, and HRQoL in older people residing in high
southern latitudes. By leveraging the Bayesian perspective, we
contribute to evidence-based strategies for promoting healthy
aging and enhancing the HRQoL of older people in regions
with pronounced seasonal variations. Our study offers a new
perspective on the implications of frailty and seasonal changes
for the well-being of older adults, addressing important gaps
in the existing literature.
While this study exhibits several strengths, it is essential
to acknowledge and address certain limitations. Chief among
these is the inherent constraint imposed by the cross-sectional
design, which restricts our ability to establish a causal relation-
ship among the variables under examination. To overcome this
limitation, future research should prioritize longitudinal stud-
ies that can delve into the temporal dynamics and bidirectional
associations among these variables. Furthermore, it is crucial
to be mindful of potential biases and subjectivity introduced
by relying on self-report measures to assess variables such
as education level, frailty, and quality of life. To bolster the
validity of our ndings, we strongly advocate for incorpor-
ating objective measurements and utilizing comprehensive
assessment tools in forthcoming research endeavors. By
providing evidence in the context of high southern latitudes,
our study offers a new perspective on the implications of
frailty for older people residing in regions with pronounced
seasonal variations.
CONCLUSION
This study reveals signicant associations between frailty,
seasonal sensitivity, and HRQoL in older people living in
high southern latitudes. The Bayesian perspective offers a
comprehensive understanding of the observed relationships,
contributing to evidence-based strategies to enhance the well-
being of older people residing in regions with pronounced
seasonal variations. These ndings have the potential to
inform targeted interventions and policies, fostering improved
health outcomes and HRQoL for this population.
ACKNOWLEDGEMENTS
We thank all study subjects for their participation, the research
team that assisted with measurements, and all the people
involved who made this work possible.
CONFLICT OF INTEREST DISCLOSURES
We have read and understood the Canadian Geriatrics Jour-
nal’s policy on conicts of interest disclosure and declare
there are none.
FUNDING
This work was funded by ANID Proyecto Fondecyt Iniciación
N°11220116.
REFERENCES
1. Partridge L, Deelen J, Slagboom PE. Facing up to the global
challenges of ageing. Nature. 2018 Sep 6;561(7721):45–56.
2. Feng Z. Global convergence: aging and long-term care policy
challenges in the developing world. J Aging Soc Pol. 2019 Aug
8;31(4):291–87.
3. Proietti M, Cesari M. Frailty: What is it? In: Veronese N, editor.
Frailty and Cardiovascular Diseases. 2020;1216:1–7.
4. Angulo J, El Assar M, Álvarez-Bustos A, Rodrıguez-Mañas
L. Physical activity and exercise: Strategies to manage frailty.
Redox Biol. 2020 Aug 1;35:101513.
5. Freer K, Wallington SL. Social frailty: the importance of social
and environmental factors in predicting frailty in older adults.
Br J Community Nurs. 2019 Oct 2;24(10):486–92.
6. Pandey A, Kitzman D, Reeves G. Frailty is intertwined with
heart failure: mechanisms, prevalence, prognosis, assessment,
and management. JACC: Heart Failure. 2019 Dec;7(12):
1001–11.

MABE-CASTRO: FRAILTY, SEASONALITY AND QUALITY OF LIFE IN OLDER PEOPLE
62
CANADIAN GERIATRICS JOURNAL, VOLUME 27, ISSUE 1, MARCH 2024
7. Kojima G, Iliffe S, Walters K. Frailty index as a predictor of
mortality: a systematic review and meta-analysis. Age Ageing.
2018 Mar 1;47(2):193–200.
8. Soysal P, Veronese N, Thompson T, Kahl KG, Fernandes BS,
Prina AM, et al. Relationship between depression and frailty
in older adults: A systematic review and meta-analysis. Ageing
Res Rev. 2017 Jul 1;36:78–87.
9. Dent E, Ambagtsheer RC, Beilby J, Stewart S. Frailty and
Seasonality. J Nutri Health Aging. 2020 Jun;24:547–49.
10. Fonte A, Coutinho B. Seasonal sensitivity and psychiatric
morbidity: study about seasonal affective disorder. BMC Psych.
2021 Jun 29;21(1):317.
11. Alvarado C, Castillo-Aguilar M, Villegas V, Estrada Goic C,
Harris K, Barria P, et al. Physical activity, seasonal sensitivity
and psychological well-being of people of different age groups
living in extreme environments. Int J Environ Res Public
Health. 2023 Jan 17;20(3):1719.
12. Gueichatureo Asencio I, Loezar Tillerıa F, Mellado Quiroz V,
Vera Vega C, Jelincic Vasquez C, Nuñez Espinoza C, et al.
[Seasonal sensitivity in a high-latitude population and its
relationship to adaptation variables and style of seasonal work
organization]. Ciencias Psicológicas. 2021 Dec;15(2). Spanish.
13. Lewy AJ, Sack RL, Singer CM, Whate DM, Hoban TM. Winter
depression and the phase-shift hypothesis for bright light’s
therapeutic effects: history, theory, and experimental evidence.
J Biologic Rhythms. 1988 Jun;3(2):121–34.
14. Levitan RD. The chronobiology and neurobiology of winter
seasonal affective disorder. Dialogues Clin Neurosci. 2007
Sep 30;9(3):315–24.
15. Wirz-Justice A. Seasonality in affective disorders. Gen Compar
Endocrinol. 2018;258:244–9
16. Centers for Disease Control and Prevention. Health-related
quality of life (HRQOL). HRQOL concepts [Internet]. 2018
[cited 2023]. Available from: https://archive.cdc.gov/#/
details?url=https://www.cdc.gov/hrqol/index.htm
17. Boggatz T. Quality of life in old age–a concept analysis. Int J
Older People Nurs. 2016 Mar;11(1):55–69.
18. Rosas-Carrasco O, Cruz-Arenas E, Parra-Rodrıguez L, Garcıa-
González AI, Contreras-González LH, Szlejf C. Cross-cultural
adaptation and validation of the FRAIL scale to assess frailty
in Mexican adults. J Am Med Dir Assoc. 2016 Dec 1;17(12):
1094–98.
19. Gleason LJ, Benton EA, Alvarez-Nebreda ML, Weaver MJ,
Harris MB, Javedan H. FRAIL questionnaire screening tool
and short-term outcomes in geriatric fracture patients. J Am
Med Dir Assoc. 2017 Dec 1;18(12):1082–86.
20. Aprahamian I, Lin SM, Suemoto CK, Apolinario D, de Castro
Cezar NO, Elmadjian SM, et al. Feasibility and factor structure
of the FRAIL scale in older adults. J Am Med Dir Assoc. 2017
Apr 1;18(4):367.e11–367.e18.
21. Puig AA, Natale V, Fabbri M. Propiedades psicométricas de
la versión castellana del cuestionario de evaluación de patrón
estacional (Seasonal Pattern Assessment Questionnaire, SPAQ).
Rev latinoam psicologıa. 2006;38(1):59–69. Spanish.
22. Urzúa A, Navarrete M. Calidad de vida en adultos mayores:
Análisis factoriales de las versiones abreviadas del WHOQoL-
old en población chilena. Rev méd Chile. 2013;141(1):28–33.
Spanish.
23. Makowski D, Ben-Shachar MS, Chen SA, Lüdecke D. Indices
of effect existence and signicance in the Bayesian framework.
Front Psych. 2019 Dec 10;10:2767.
24. Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FS.
Frailty and pre-frailty in middle-aged and older adults and its
association with multimorbidity and mortality: a prospective
analysis of 493,737 UK Biobank participants. The Lancet Public
Health. 2018 Jul 1;3(7):e323–e332.
25. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et
al. Modiable risk factors, cardiovascular disease, and mortality
in 155,722 individuals from 21 high-income, middle-income,
and low-income countries (PURE): a prospective cohort study.
The Lancet. 2020 Mar 7;395(10226):795–808.
26. Dugravot A, Fayosse A, Dumurgier J, Bouillon K, Rayana TB,
Schnitzler A, et al. Social inequalities in multimorbidity, frailty,
disability, and transitions to mortality: a 24-year follow-up
of the Whitehall II cohort study. The Lancet Public Health.
2020;5(1):e42–e50.
27. Welmer AK, Kåreholt I, Rydwik E, Angleman S, Wang HX.
Education-related differences in physical performance after age
60: a cross-sectional study assessing variation by age, gender
and occupation. BMC Public Health. 2013 Dec;13(1):1–3.
28. Hoogendijk EO, van Hout HP, Heymans MW, van der Horst
HE, Frijters DH, van Groenou MI, et al. Explaining the asso-
ciation between educational level and frailty in older adults:
results from a 13-year longitudinal study in the Netherlands.
Ann Epidemiol. 2014 Jul 1;24(7)538–44.
29. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty
in elderly people. The Lancet. 2013 Mar 2;381(9868):752–62.
30. Kojima G, Iliffe S, Jivraj S, Walters K. Association between
frailty and quality of life among community-dwelling older
people: a systematic review and meta-analysis. J Epidemiol
Community Health. 2016 Jan 18;70(7):716–21.
31. Hoogendijk EO, Suanet B, Dent E, Deeg DJ, Aartsen MJ.
Adverse effects of frailty on social functioning in older adults:
results from the Longitudinal Aging Study Amsterdam. Maturi-
tas. 2016 Jan 1;83:45–50.
32. Rosenberg JM, Kubsch M, Wagenmakers E-J, Dogucu M.
Making sense of uncertainty in the science classroom. Science
& Education [Internet]. 2022 Oct 1;31(5):1239–62. Available
from: https://doi.org/10.1007/s11191-022-00341-3
33. Fornacon-Wood I, Mistry H, Johnson-Hart C, Faivre-Finn
C, O’Connor JPB, Price GJ. Understanding the differences
between Bayesian and frequentist statistics. Int J Radiat Oncol
Biol Phys [Internet]. 2022 Apr 1;112(5):1076–82. Available
from: https://doi.org/10.1016/j.ijrobp.2021.12.011
34. Schmid D, Stanton NA. Exploring Bayesian analyses of a small-
sample-size factorial design in human systems integration: the
effects of pilot incapacitation. Hum-Intell Syst Integrat [Inter-
net]. 2019;1(2):71–88. Available from: https://doi.org/10.1007/
s42454-020-00012-0
Correspondence to: Cristian Núñez-Espinosa, PhD, School
of Medicine, Magallanes University, Avenida Bulnes 01855,
Box 113-D, Punta Arenas, Chile
E-mail: cristian.nunez@umag.cl