LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades, Asunción, Paraguay.
ISSN en línea: 2789-3855, febrero, 2025, Volumen VI, Número 1 p 1478

DOI: https://doi.org/10.56712/latam.v6i1.3429

Psychological and physical variables to predict quality of life
in patients undergoing hematopoietic stem cell

transplantation: cross-sectional research
Variables psicológicas y físicas para predecir calidad de vida en pacientes
sometidos a trasplante hematopoyético de células progenitoras: estudio

transversal

Liliana Rivera Fong
lmeylenf@hotmail.com

https://orcid.org/0000-0003-4155-9954
Instituto Nacional de Cancerología. Universidad Nacional Autónoma de México

Ciudad de México – México

Corina Benjet
cbenjet@gmail.com

https://orcid.org/0000-0002-4569-6094
Instituto Nacional de Psiquiatría

Ciudad de México – México

Rebeca Robles García
reberobles@hotmail.com

https://orcid.org/0000-0001-5958-7393
Instituto Nacional de Psiquiatría

Ciudad de México – México

José Luis Aguilar Ponce
joluagpo@gmail.com

https://orcid.org/0000-0002-9960-8547
Instituto Nacional de Cancerología

Ciudad de México – México

Brenda Lizeth Acosta Maldonado
brenda_ao@hotmail.com

https://orcid.org/0000-0003-4692-0710
Instituto Nacional de Cancerología

Ciudad de México – México

Angélica Riveros Rosas
vercige52@gmail.com

https://orcid.org/0000-0002-4030-3407
Universidad Nacional Autónoma de México

Ciudad de México – México

Artículo recibido: 28 de enero de 2025. Aceptado para publicación: 10 de febrero de 2025.
Conflictos de Interés: Ninguno que declarar.

Abstract
The aim of this study was to identify psychological and physical correlates of Quality of Life (QOL) in
patients undergoing hematopoietic stem cell transplantation (HSCT). A total of 146 adult patients
participated, of which 67.12% receiving an autologous HSCT and 32.87% an allogeneic HSCT. The
sample included 63.01% male patients with a mean age of 40.24 years (s.d.=14.03). This was a cross-
sectional design in which QoL was assessed using the Functionality Assessment of Cancer Therapy

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ISSN en línea: 2789-3855, febrero, 2025, Volumen VI, Número 1 p 1479

– Bone Marrow Transplant (FACT-BMT). Independent variables included physical symptoms,
emotional distress, coping, and problem-solving style. Multiple regression analyses were performed
for each QoL sub-scale with variables that showed correlation. The analysis showed the global QoL
mean was 83.33 (s.d.=13.47) on a scale from 0 to 108. Descriptive analyses of the QoL subscales
showed greater negative impact on functional well-being. In contrast, physical well-being was the least
affected subscale. Of the eight regression models (one for each QoL sub-scale) between 24.4% and
71.6% of the total variance was explained. Models included eight independent variables, the most
strongly associated were fatigue, depression, and anxious preoccupation. In conclusion we identified
medical and psychological correlates of patients’ QoL. While this design cannot establish causal
relations, establishing these correlates is relevant for clinical practice to identify patients with higher
risk for compromised QoL and to guide the development of clinical interventions to promote protective
QoL factors.

Keywords: quality of life, functionality, hemato-oncology, wellbeing, Mexico

Resumen
El objetivo de este estudio fue identificar si variables médicas y psicológicas tienen correlación con la
calidad de vida (CV) en pacientes sometidos a trasplante hematopoyético de células progenitoras
(TCPH). Se incluyeron 146 pacientes adultos, de los cuales, el 67.12% recibió un TCPG autólogo y el
32.87% alogénico. El 63.01% de los participantes fueron hombres y la muestra tuvo una media de edad
de 40.24 años (d.e.=14.03). Se trató de un estudio transversal donde se evaluó CV con el Functionality
Assessment of Cancer Therapy – Bone Marrow Transplant (FACT-BMT). Como variables
independientes se incluyeron síntomas físicos, distress emocional y estilos de afrontamiento y de
solución de problemas. Se llevó a cabo un análisis de regresión múltiple para cada subescala de CV
con las variables que mostraron correlación. Los análisis mostraron que la CV promedio fue de 83.33
(d. e.=13.47) en una escala de 0 a 108. Los análisis descriptivos de las escalas de CV mostraron mayor
impacto negativo en bienestar funcional; mientras que bienestar físico fue la escala menos afectada
según el reporte de los pacientes. Se realizaron ocho modelos de regresión (uno para cada sub-
escala), los cuales mostraron entre el 24.4% y 71.6% del total de varianza explicada. Los modelos
incluyeron ocho variables independientes, de las cuales fatiga, depresión y preocupación ansiosa
fueron las que mostraron las asociaciones más fuertes. En conclusión, se identificaron importantes
correlaciones de CV con variables médicas y psicológicas; las cuales deben ser abordadas ya que, si
bien el diseño no permite establecer relaciones causales, son hallazgos relevantes para la práctica
clínica al permitir la identificación de posibles pacientes con mayor riesgo de compromiso o impacto
negativo en la CV. Estos hallazgos pueden servir como guía para el desarrollo de intervenciones
clínicas enfocadas en la promoción de estas variables con el fin de prevenir el impacto en la calidad
de vida.

Palabras clave: calidad de vida, funcionalidad, hemato-oncología, bienestar, México

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ISSN en línea: 2789-3855, febrero, 2025, Volumen VI, Número 1 p 1480

Todo el contenido de LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades,
publicado en este sitio está disponibles bajo Licencia Creative Commons.

Cómo citar: Rivera Fong, L., Benjet, C., Robles García, R., Aguilar Ponce, J. L., Acosta Maldonado, B. L.,
& Riveros Rosas, A. (2025). Psychological and physical variables to predict quality of life in patients
undergoing hematopoietic stem cell transplantation: cross-sectional research. LATAM Revista
Latinoamericana de Ciencias Sociales y Humanidades 6 (1), 1478 – 1495.
https://doi.org/10.56712/latam.v6i1.3429

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ISSN en línea: 2789-3855, febrero, 2025, Volumen VI, Número 1 p 1481

INTRODUCTION

The aim of Hematopoietic Stem Cell Transplantation (HSCT) is to cure or control the primary illness of
hemato-oncological patients and improve long-term quality of life (QoL) comparable to that of the
general population. However, as much as three years after an HSCT, patients report lower QoL than the
general population (Sutherland, Fyles, Adams, et al., 1997). Cross-sectional research has indicated that
50% of these patients experience physical negative effects. Additionally, 40% reported emotional
impact and 42% mentioned social consequences before undergoing HSCT (Pillay, Lee, Katona, et al.,
2014a). Moreover, the negative impact upon QoL persists or even increases in approximately 46% of
patients after the procedure (Beeken, Eiser, & Dalley, 2011), thus, achieving acceptable QoL standards
is not an easy task.

Regarding long-term impact, a study has compared patients’ QoL previous to HSCT with patients’ QoL
at different times after the procedure. These results indicate that after three years, patients do not
improve their physical QoL but show small improvements in emotional QoL (x=56 vs. x=64), social
(x=55 vs. x=60), and spiritual QoL (x=72 vs. x=75) (Wong, Francisco, Togawa et al., 2010). Another study
has indicated that at ten years after an HSCT, survivors have lower QoL levels than a general population
group. Specifically, survivors have lower levels of mental components (p=0.01), including emotional
role, social function, mental health and vitality; as well as lower levels in the physical component (p =
0.02), which include physical function, role physical, bodily pain and general health (Syrjala, Langer,
Adrams, et al., 2005).

Given the importance of QoL above and beyond a medical cure, significant efforts have sought to
identify the factors that contribute to or are associated with QoL. Previous studies have reported
negative associations between QoL and anxious or depressive symptomatology (Pillay, Lee, Katona, et
al., 2014b) and helplessness/hopelessness coping style (Pillay et al., 2014b). On the other hand, higher
QoL levels have been associated with a fighting spirit coping style (Pillay et al., 2014b), better self-
efficacy (Hochhausen, Altmaier, McQuellon, et al., 2007), optimism (Hochhausen et al., 2007), and
higher social support (Hochhausen et al., 2007). In another study, QoL related to the HSCT was
predicted through severity of depression ß = -0.497, t=-0.039, p < 0.001) or anxiety (ß = -0.365, t = -
4.430, p < 0.001); each one with an independent model (Janicsák, Masszi, Reményi, et al., 2013).

A longitudinal study followed HSCT patients for seven years and found a direct effect that indicated
that an increase in physical symptoms by 1 SD was significantly associated with a decrease in physical
HRQOL by 0.98 SD and an increase in depressive symptoms by 0.94 SD (b = 0.94). In addition, an
increase in depressive symptoms by 1 SD was significantly associated with a decrease in mental
HRQOL by 0.26 SD (b=-0.26) and increase in depressive symptoms by 0.86 SD (b=0.86), and, greater
depressive symptoms were significantly associated with poorer mental HRQOL (b=-0.72) (Kenzik,
Huang, Rizzo et al., 2015). Furthermore, physical symptoms (b = -0.23), depressive symptoms (b = -
0.85), and avoidant coping style (b = 0.13) directly influenced mental QoL. Simultaneously, the latter
two variables acted as mediators of optimism and physical symptoms in predictive models of mental
QoL (CFI = 0.94, RMSEA = 0.03) (Kenzik, et al., 2015).

These aforementioned studies have been conducted in HSCT patients in mostly the US or European
high-income countries. Given QoL is highly influenced by cultural aspects (World Health Organization,
1997), sociodemographic and cultural differences may limit the external validity of such studies in
diverse cultures and lower-income settings, like Mexico. Thus, this study aims to identify what set of
psychological variables (anxiety, depression, distress, mental adjustment to cancer, or problem-solving
strategy) and side effects of treatment are associated with QoL among Mexican patients undergoing
HSTC.

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ISSN en línea: 2789-3855, febrero, 2025, Volumen VI, Número 1 p 1482

METHODOLOGY

Sample and Procedures

The present cross-sectional study involved 146 patients in different phases of an autologous or
allogeneic HSCT, all of them treated in a public hospital in Mexico City. See Table 1 for characteristics
of the patients. The sample size was calculated based on the total number of patients seen in the
hospital in the last 15 years (302 patients). A 5% margin of error and a 90% confidence interval were
considered.

A psychologist reviewed medical records and identified patients who met the inclusion criteria (hemato-
oncological patients aged 18 years or older who received or were about to receive HSCT). Potential
participants were recruited in the Bone Marrow Transplantation Unit (BMTU) or before their medical
consultation. The psychologist introduced herself, explained the study's objective, and explained how
the patient would participate. Patients who agreed to participate signed informed consent and
responded to the self-administered instruments. The Research and Ethics Committees of the hospital
approved the protocol. Upon obtaining consent, the psychologist reviewed patients' clinical information
based on an ad hoc questionnaire developed for this study, including diagnoses, clinical response, and
type of transplant.

Table 1

Socio-demographic characteristics of patients

f (%) Transplant type f (%)
Gender 86 (58.9) Autologous 98 (67.12)

60 (41.1) Allogeneic 48 (32.87)

Age Evaluation time f (%)
Mean 40.24 Candidate to HSCT 42 (28.76)
Standar Deviation 14.03 Hospitalized to HSCT 38 (26.03)
Range 18 – 68 Follow up 66 (45.21)

Marital status Cancer diagnosis f (%)
Single 54 (37.0) Acute Leukemia 32 (21.92)
Married 54 (37.0) Non-Hodgkin Lymphoma 48 (32.88
Common-law union 13 (8.9) Hodgkin Lymphoma 16 (10.95)
Divorced 16 (11.0) Multiple Myeloma 36 (24.66)
Separated 9 (6.2) Myelodysplastic Syndrome 1 (0.68)

Measures

The study included five instruments. We measured the independent variable (QoL) with the Spanish
version of the Functionality Assessment of Cancer Therapy – Bone Marrow Transplantation (FACT –
BMT) that was previously adapted for the Mexican population (Rivera-Fong, Benjet, Robles, et al., 2020).
This adapted instrument had adequate adjustment with its original structure and internal consistency
(Cronbach’s Alpha = 0.900). The FACT-BMT includes 50 items grouped into five domains: (a) Physical
well-being (PWB), (b) Functional well-being (FWB), (c) Emotional well-being (EWB), (d) Social and family
well-being (SWB) and, (e) Bone Marrow Transplantation scale (BMT).

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Regarding the independent variables, all instruments were validated for the Mexican population; and
authors selected instruments that prioritize objective symptoms evaluation. Anxiety and depression
were evaluated with the 12-item Hospital Anxiety and Depression Scale (HADS). In a prior study with
Mexican oncological patients, this scale explained 48% of the variance and had good internal
consistency (α = 0.86) (Galindo, Benjet, Juárez, et al., 2015).

We evaluated overall distress with the Distress Thermometer (DT), this instrument has two parts: the
first one includes a single item thermometer-shaped visual analog scale self-rated from 0 to 10, which
has shown 93% sensitivity and 76% specificity in Mexican cancer patients (Almanza, Rosario, & Pérez,
2008). The second part of the DT contains items to evaluate spiritual/religious concerns and practical,
family, physical, and emotional problems; all items refer to problems or symptoms associated with
cancer or its treatment; including HSCT´s most frequent side effects in immediate (i.e. nausea or
vomiting, sores in the mouth, fatigue, and diarrhea) and long-term (i.e. cataracts, sexual side effects,
thyroid problems, and lung or bone damage (Almanza et al., 2008).

We evaluated patients´ problem-solving resources with the Social Problem Solving Instrument (SPSI).
The Mexican adaptation of the SPSI is composed of 25 items grouped into four social problems
solution styles: (a) rational, (b) avoidance, (c) impulsive – carelessness, and (d) negative orientation
(14). It has shown adequate internal consistency (α = 0.86) and explained 50.22% of the variance.

Finally, we measured adjustment to cancer with the Mental Adjustment to Cancer instrument (MAC).
The MAC includes 22 items that measures five domains: (a) fighting spirit, (b) anxious preoccupation,
(c) helplessness / hopelessness, (d) positive attitude, and (e) cognitive avoidance. In Mexican patients,
it explains 53% of total variance with an internal consistency of α = 0.78 (Galindo, 2019).

Data analysis

First, we performed a descriptive analysis of all the dependent and independent variables in which we
computed the mean, median, mode, and dispersion measurements (standard deviation and range) in
all scales. Then, as a preliminary analysis to determine which variables to include in the final models,
we ran correlations among all subscale variables using Pearson's r coefficient. Variables that showed
statistical significance (p < 0.05) and a minimum magnitude of association of >0.40 with each subscale
of QoL were included in the subsequent analysis. Furthermore, to confirm the variables' association
with QoL, we created two groups using the extreme quartiles of quality of life scores and compared all
variables using independent samples Student t tests.

Finally, for each QoL subscale, we identified variables that met correlation and comparison criteria
(relevant correlation >0.40) and performed multiple regression analyses. The predictive analysis
included each variable's significance and total variance.

RESULTS

Table 2 shows patients’ scores in quality of life and all the independent variables. Functional well-being
was the most affected area of QoL. In contrast, physical well-being was the least affected.

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Table 2

Quality of life evaluation with the FACT-BMT instrument in patients undergoing to Hematopoietic Stem Cell Transplantation

Evaluation topic Instrument’s range Participants’ range Mean (s.d.) Median (interquartile
range)

FACT – BMT FACT-G 0 – 108 43 - 106 83.88 (13.47) 85 (76 - 94)
PWB 0 – 28 5 – 28 22.93 (4.62) 24 (21 - 26)
SWB 0 – 28 8 – 28 21.45 (4.55) 22 (18 - 25)
EWB 0 – 24 6 – 24 19.42 (3.54) 20 (17 - 22)
FWB 0 – 28 6 – 28 20.08 (5.37) 20 (16 - 24)
BMTS 0 – 40 13 – 40 28.24 (5.87) 28 (24 - 33)
TOI 0 – 96 28 – 95 71.25 (13.77) 73 (64 - 81)
FACT-BMT 0 – 148 57 - 145 112.12 (18.24) 113 (101 - 125)

HADS Anxiety 0 – 18 0 – 14 2.71 (2.6) 2 (1 - 4)
Depression 0 – 18 0 – 9 2.01 (2.4) 1 (0 - 3)
Total 0 – 32 0 – 22 4.72 (4.3) 4 (2 - 7)

Distress thermometer Distress Thermometer 0 – 10 0 – 10 2.60 (2.7) 2 (0 - 4)
Diary problems 0 – 5 0 – 5 1.24 (1.2) 1 (0 - 2)
Family problems 0 – 2 0 – 2 0.22 (0.5) 0 (0 - 0)
Emotional problems 0 – 7 0 – 7 1.80 (1.6) 1.5 (0 - 3)
Physical problems 0 – 21 0 – 13 4.30 (3.7) 3 (1 - 7)

Mental adjustment to cancer Fighting spirit 0 – 100 58.33 - 100 86.13 (11.3) 87.5 (78.1 – 95.8)
Anxious preoccupation 0 – 100 25 – 90 45.55 (15.4) 40 (35 - 55)
Helplessness/Hopelessness 0 – 100 25 – 100 38.46 (15.5) 35 (25 - 45)
Positive Attitude 0 – 100 25 – 100 86.47 (16.0) 91 (75 - 100)
Cognitive Avoidance 0 – 100 33.33 – 100 71.97 (15.5) 75 (58.3 – 83.3)

Problem solving Rational solution 0 – 100 20 – 100 67.53 (18.3) 67.69 (55 – 81.5)
Negative orientation 0 – 100 20 – 93.33 38.90 (17.9) 33.33 (26.7 – 46.7)
Avoidance style 0 – 100 20 – 83.33 32.81 (13.3) 30 (23.3 – 36.7)
Impulsive – carelessness 0 – 100 20 – 93.33 37.90 (15.4) 33.33 (26.7 – 46.7)

Symptomatology Fatigue 0 – 100 0 – 100 23.14 (22.3) 22.22 (0 – 33.3)
Sickness 0 – 100 0 – 100 6.85 (16.0) 0 (0 - 0)
Pain 0 – 100 0 – 100 15.07 (22.1) 0 (0 – 33.3)
Dyspnea 0 – 100 0 – 100 6.39 (17.21) 0 (0 - 0)

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Insomnia 0 – 100 0 – 100 18.95 (25.9) 0 (0 – 33.3)
Loss of appetite 0 – 100 0 – 100 10.73 (19.9) 0 (0 – 33.3)
Constipation 0 – 100 0 – 100 11.64 (21.7) 0 (0 – 33.3)
Diarrhea 0 – 100 0 – 100 8.45 (18.7) 0 (0 - 0)
Financial problems 0 – 100 0 – 100 48.17 (34.8) 33.33 (33.3 – 66.7)

Note: FACT-G = Functional Assessment of Cancer Therapy – General; PWB = Physical Wellbeing, SWB = Social Wellbeing; EWB = Emotional Wellbeing, FWB =
Functional Wellbeing; BMTS = Bone Marrow Transplantation Scale; TOI = Trial Outcome Index; FACT-BMT = Functional Assessment of Cancer Therapy - Bone
Marrow Transplantation. HADS: Hospital Anxiety and Depression Scale.

Table 3 shows the correlations among the independent variables and QoL. Global QoL had a relevant negative correlation with depression, distress, emotional
problems, physical problems, anxious preoccupations, negative orientation, fatigue, and pain.

Table 3

Correlations among Quality of Life and independent variables

PWB SWB EWB FWB BMTS TOI FACT-G FACT.BMT
HADS Anxiety -0.273** -0.183* -0.478**£ -0.254** -0.321** -0.382** -0.386** -0.386**

Depression -0.285** -0.425**£ -0.490**£ -0.504**¥ -0.46**£ -0.486**£ -0.571**¥ -0.568**¥
Total -0.321** -0.343** -0.557**¥ -0.429**£ -0.44**£ -0.464**£ 0.543**¥ -0.544**¥

TD Distress thermometer -0.286** -0.354** -0.417**£ -0.321** -0.245** -0.325** -0.455**£ -0.415**£
Diary problems -0.198* -0.191* -0.266 -0.327** -0.265** -0.307** -0.333** -0.331**
Family problems -0.125 -0.336** -0.086 -0.085 -0.103 -0.119 -0.213** -0.190*
Emotional problems -0.377** -0.309** -0.620**¥ -0.353** -0.43**£ -0.446**£ -0.537**¥ -0.534**¥
Physical problems -0.681**¥ -0.281** -0.341** -0.428**£ -0.44**£ -0.583 -0.589**¥ -0.576**¥

MAC Fighting spirit 0.125 0.406**£ 0.187* 0.396** 0.279** 0.315** 0.387** 0.375**
Anxious preoccupations -0.217** -0.337** -0.574**¥ -0.290** -0.43**£ -0.370** -0.455**£ -0.455**£
Helplessness / Hopelessness -0.128 -0.376** -0.337** -0.319** -0.342** -0.313** -0.387** -0.396**
Positive attitude -0.047 0.261** 0.148 0.148 0.093 0.081 0.170 0.155
Cognitive avoid 0.306** 0.250** 0.149 0.377** 0.381** 0.412**£ 0.379** 0.402**£

SPS Rational solution 0.120 0.350** 0.183* 0.261** 0.247** 0.247** 0.312** 0.310**
Negative orientation -0.401**£ -0.115 -0.528**¥ -0.295** -0.378** -0.411**£ -0.433**£ -0.441**£
Avoidance style -0.177* -0.147 -0.465**£ -0.162 -0.230** -0.220** -0.297** -0.293**

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Impulsive – carelessness -0.206* 0.013 -0.225** -0.091 -0.145 -0.166* -0.162 -0.166*
EORTC Fatigue -0.780**¥ -0.232** -0.363** -0.534**¥ -0.62**¥ -0.731**¥ -0.654**¥ -0.681**¥

Sickness -0.422**£ 0.052 -0.126 -0.137 -0.210* -0.284** -0.215** -0.226**
Pain -0.669**¥ -0.104 -0.262** -0.336** -0.370** -0.513**¥ -0.467**£ -0.464**£
Dyspnea -0.353** 0.000 -0.138 -0.282** -0.279** -0.347** -0.270** -0.298**
Insomnia -0.332** -0.088 -0.244** -0.328** -0.226** -0.336** -0.339** -0.323**
Loss of appetite -0.359** -0.112 -0.321** -0.234** -0.293** -0.336** -0.339** -0.344**
Constipation -0.134 -0.027 -0.280** -0.054 -0.123 -0.119 -0.150 -0.151
Diarrhea -0.424**£ -0.044 -0.068 -0.060 -0.104 -0.210* -0.202* -0.183*
Financial problems -0.367** -0.156 -0.388** -0.281** -0.332** -0.374** -0.393* -0.397**

Note: *p < 0.05; ** p < 0.01; £moderate correlation; ¥high correlation. PWB=Physical Wellbeing; SWB=Social Wellbeing; EWB=Emotional Wellbeing;
FWB=Functional Wellbeing; BMTS=Bone Marrow Transplantation Scale; TOI=Outcomes Index; FACT-G=Functional Assessment of Cancer Therapy – General;
FACT-BMT=Functional Assessment of Cancer Therapy – Bone Marrow Transplant. TD=Distress Thermomether; MAC=Mental Adjustment of Cancer;
SPS=Social Problem Solving; EORTC=Quality of Life.

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Subsequently, regression analyses were performed with independent variables that showed
correlations >0.40 and each subscale of QoL by prioritizing parsimonious models. We tested a model
for physical well-being, including fatigue (b = -0.493, p < 0.001) (FIV = 2.09), physical problems (b = -
0.219, p < 0.001) (FIV = 1.83), pain (b = -0.171, p = 0.006) (FIV = 1.91), diarrhea (b = -0.178, p < 0.001)
(FIV = 1.19). Nausea (p = 0.321) and negative orientation (p = 0.251) lost significance. After eliminating
them, the model was statistically significant (F = 92.35; p < 0.001), and it explained 71% of the variance.
For social well-being, a multiple regression analysis, including depression (b = -0.321, p < 0.001) (FIV =
1.15) and fighting spirit mental adjustment to cancer (b = -0.291, p < 0.001) (FIV = 1.15), showed a
statistically significant impact (F = 24.44; p < 0.001), which explained 24.4% of the variance. Fighting
spirit includes items such “I am determined to face everything” or “I focus on the good things I have”

In the case of emotional well-being, we tested a model with seven variables that showed relevant
correlations but lost significance as predictors; anxiety (p = 0.937), depression (p = 0.204), distress (p
= 0.856), negative orientation problem-solving style (p = 0.152) and avoidant/ carelessness orientation
problem-solving style (p = 0.421). A new, statistically significant model (F=63.80; p < 0.001) explained
46.4% of variance; this model included number of emotional problems (b = -0.441, p < 0.001) (FIV =
1.37) and an anxious preoccupation mental adjustment to cancer (b = -0.346, p < 0.001) (FIV = 1.37),
such as “My health problems prevent me from planning for the future” or “I don’t know what I have to
do” or “I’m worried that I experience a cancer relapse or the cancer will get worse”.

For functional well-being, the initial analysis included depression, fatigue, and the number of physical
symptoms. However, physical symptoms did not show statistical significance (p = 0.315), so it was
removed. After this adjustment, the new model explained 41.3% of the variance and showed statistical
significance (F=52.03; p < 0.001) with depression (b = -0.384, p < 0.001) (FIV = 1.09) and fatigue (b = -
0.426, p < 0.001) (FIV = 1.09).

For the bone marrow transplant subscale, a model with five variables was tested. The number of
emotional problems (p = 0.543) and the number of physical issues (p = 0.671) were not significant.
After removing these variables, a model that proved to be statistically significant (F = 28.06; p < 0.001)
resulted and explained 48.8% of the variance by depression (b = -0.228, p < 0.001) (FIV = 1.27), anxious
preoccupations (b = -0.210, p = 0.002) (FIV = 1.25), and fatigue (b = -0.496, p < 0.001) (FIV = 1.11).

The FACT-BMT instrument includes a sub-scale named Trial Outcome Index (TOI); this index is the
summed score of the physical well-being scale and the functionality well-being score; thus, TOI has
been considered the most appropriate single patient-reported indicator by some clinical research. Fon
this index we tested a model with seven variables. Still, the number of emotional problems (p = 0.536),
negative orientation of social problem-solution style (p = 0.894), and pain (p = 0.706) were not
significant; so they were removed. After this adjustment, we obtained a model that explained 66.1% of
the variance (F = 71.74; p < 0.001); the model includes depression (b = -0.245, p < 0.001) (FIV = 1.16),
number of physical problems (b = -0.172, p = 0.006) (FIV = 1.65), fatigue (b = -0.505, p < 0.001) (FIV =
1.69), cognitive avoidance (b = -0.199, p < 0.001) (FIV = 1.10) which contains items such “I don’t really
think I have cancer” or “I try to keep living like I always have”.

As for the global quality of life scale (FACT-G) a model with eight variables was tested, however the
distress thermometer score (p = 0.209), number of emotional problems (p = 0.837), negative orientation
problem-solving style (p = 0.557), and pain level (p = 0.856) lost significance as predictors. A
statistically significant model was obtained (F = 62.76; p < 0.001), which explains 63% of the variance
through depression (b = -0.327, p < 0.001) (FIV = 1.29), number of physical problems (b = -0.204, p =
0.002) (FIV = 1.65), anxious preoccupation (b = -0.163, p = 0.005) (FIV = 1.26), and fatigue (b = -0.396,
p < 0.001) (FIV = 1.63).

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Finally, for the total scale of QoL (FACT-BMT) we performed an initial multiple regression analysis with
nine variables, but DT score (p = 0.727), number of emotional problems (p = 0.988), negative orientation
problem-solving style (p = 0.696), and pain (p = 0.759) lost predictive significance. Upon removing these
variables, a model explained 67.7% of the variance and was statistically significant (F = 61.84; p <
0.001). The predictive model includes depression (b = -0.291, p < 0.001) (FIV = 1.32), physical problems
(b = -0.172, p = 0.005) (FIV = 1.66), anxious preoccupation (b = -0.167, p = 0.002) (FIV = 1.27), fatigue
(b = -0.407, p < 0.001) (FIV = 1.13), and cognitive avoidance (b = -0.167, p = 0.002) (FIV = 1.27).

Figure 1 shows the integration of models obtained by multiple regression analyses, the explained
variances of each factor and the psychological and medical variables that proved significant impact for
each model.

Figure 1

Physical and psychological variables to predict quality of life and its factors

Note: PWB=Physical Wellbeing; SWB=Social Wellbeing; EWB=Emotional Wellbeing; FWB=Functional
Wellbeing; BMTS=Bone Marrow Transplantation Scale; TOI=Outcomes Index (PWB+FWB+10 items of

PWB (71.6%)
71.6%

SWB (24.4%)

EWB (46.4%)

Fighting spirit

Diarrhea

Emotional
problems

FWB (41.3%)
Depressio

Fatigue

BMT (48.8%)

Anxious
preoccupation

Cognitive

avoide
TOI (66.1%)

FACT-G
(66.3%)

FACT-BMT

(67.7%)

Physical
problemas

Pain

b > 0.001 < 0.199
b > 0.200 < 0.299
b > 0.300 < 0.399
b > 0.400 < 0.499
b > 0.500

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BMT); FACT-G=Functional Assessment of Cancer Therapy – General (PWB+FWB+SWB+EWB); FACT-
BMT=Functional Assessment of Cancer Therapy – Bone Marrow Transplant (FACT-G + 10 items BMT).

DISCUSSION

This study's objective was to evaluate a set of psychological and medical correlates of quality of life in
Mexican patients undergoing an HSCT. Results indicated that Global QoL were correlated with
psychological and physical variables that include depression, anxious preoccupations, cognitive
avoidance, physical problems, and fatigue.

However, specific areas showed correlations with different variables. An interesting result is that
Physical QoL only correlated with physical side effects associated with treatment or illness. On the
other hand, social and emotional QoL only showed correlations with emotional symptoms or coping
style, but functional QoL showed correlation with physical and psychological variables.

Similar results were identified in previous studies that identified various degrees of association and
prediction of physical quality of life by (a) the presence and intensity of physical symptoms such as
fatigue (Boland, Eiser, Ezaydi, et al., 2012; Gielissen, Schattenberg, Verhagen, et al., 2007), pain
perceived (Boland, et al., 2012), physical symptoms (Kenzik et al., 2015); (b) variables related to
treatment and disease such as the intensity of treatment received (Wingard, Huang, Sobocinski, et al.,
2010), the experience of severity of the HSCT (18), the development of graft-versus host disease
(GVHD) (Wingard et al., 2010; Rosenberg, Syrjala, Martin, et al., 2015); the requirement of more than
four medications (Rosenberg et al., 2015) and ; (c) variables on the patient's global health status as
general health (Wingard et al., 2010), the presence and number of comorbidities (Wingard et al., 2010),
low Karnofsky evaluation (Wingard et al., 2010), physical limitations (Kenzik et al., 2015), and functional
status (Rosenberg et al., 2015). On the other hand, this study did not find predictive power of physical
symptoms with variables identified in previous studies, such as anxiety (Goetzmann, Klaghofer,
Wagner-Huber, et al., 2007; Wingard et al., 2010), depression (Pillay et al., 2014b; Goetzmann et al.,
2007) or fighting spirit as a mental adjustment to cancer (Pillay et al., 2014b).

In this sense, symptoms like fatigue and pain should be routinely evaluated because fatigue has shown
prevalence greater than 60% up to 6 months after a transplant (Costanzo, Knight, Coe et al., 2020); and
pain increase between 47% and 71% after a HSCT in contrast with prevalence before the procedure
(Galvin, Paice, & Mehta, 2015). Additionally, these symptoms have been associated with physical and
emotional QoL, but we only identified correlation with physical QoL.

The variables that demonstrated predictive power for emotional QoL included emotional problems and
anxious preoccupations with mental adjustment to cancer. Both variables have been previously
identified in similar studies with other groups of variables such as social limitations (Kenzik et al., 2015),
impairments in global mental health (Vickberg, DuHamel, Smith et al., 2001), and the presence of
anxiety personality traits (Wingard et al., 2010) as precursors of greater effects on the emotional area.
On the other hand, the present study did not identify that depressive symptoms (Kenzik, et al., 2015;
Loberiza Jr, Rizzo, Bredeson et al., 2002), avoidant problem-solving style (Kenzik et al., 2015) could
predict QoL like previous studies. However, all patients should be evaluated by a mental health
professional because a patient’s mental health before a transplant could be correlated with mental
health after the procedure.

Furthermore, it is possible to predict social QoL by depressive symptoms and fighting spirit style of
mental adjustment to cancer; both were previously identified (Pillay et al., 2014b). However, no
predictive function was found with other reported variables such as social functioning (Vickberg et al.,
2001), fatigue (Gielissen et al., 2007), and anxiety (Wingard et al., 2010).

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According to the descriptive analysis of this study it is important to note that each QoL subscale median
is above the midpoint of each subscale; in contrast the medians shown in the scales to evaluate
psychological symptoms or physical problems are below the midpoint of each subscale. A possible
explanation is that patients who received an HSCT in Mexico are carefully evaluated to ensure patients
have the personal resources, social support, and sociodemographic conditions to handle a transplant.
This evaluation may have excluded patients with risk factors to develop complications or severe
medical conditions; for example, the mean of level education in the sample is higher than the general
population, all patients have social support, and patients did not pay for the procedure or
hospitalization.

In addition, it is important to underline that the two more common mental adjustments to cancer coping
styles used in this sample were fighting spirit and positive attitude. These coping styles were
associated with better functionality and higher QoL in other studies (Pillay et al., 2014a; Pillay et al.,
2014b; Hochhausen et al., 2007). Given that most of these patients used these coping styles it was not
possible to evaluate the effect of other coping styles.

Finally, an international study suggests that Latin-American patients have lower QoL in contrast with
the Caucasan American population; this study identified cultural variables which could impact QoL like
familism (needs and objectives of the family are placed over the needs of the individual), simpatía (a
preference towards pleasant and non-confrontational social interactions that could facilitate patients
to obey medical indications in order to avoid the displeasure of health professionals), and religiosity /
spirituality (given Hispanics often consider religion as a source of support during health adversities)
(Yanez, McGinty, Buitrago et al., 2016).

This study contributes additional insight into factors associated with QoL in patients undergoing an
HSCT, literature that is considered weak or inconclusive and that until now has not included samples
from Latin America (Braamse, Gerrits, van Meijel et al., 2012). Findings from the present study and
previous evidence suggest that variables such as physical discomfort, anxiety, depression, and coping
styles are important correlates of patients' quality of life undergoing HSCT. However, as a limitation,
the cross-sectional design of this study precludes inferring directionality or causality. While determining
correlates of QoL it is important to identify those patients that may be at high risk for poor QoL or for
not regaining levels of QoL; future research to provide evidence for causality through longitudinal
studies is important to determine whether modifying these variables will have an impact upon QoL.

While we present data from a population (Mexican) with little prior research in this area, a further
limitation of this study, the limited sample size from one public hospital, precludes generalizing the
findings to all Mexican patients treated with HSCT. We recommend developing both multicenter and
longitudinal studies.

Finally, we focused on medical and psychological variables, but future research could include variables
such as nutritional status and social support which have been associated with QoL in oncological
patients.

CONCLUSION

Quality of life must be evaluated throughout the disease process because it is a dynamic and
multicomponent construct, so it is necessary to identify conditions that can be treated in each phase
of medical attention. The prompt intervention can reduce the overall impact of the disease and its
treatment.

In this research, patients had a hemato-oncological disease and received complex medical treatment
such as a HSCT; but the main area of impairment was the functionality or role fulfillment, not the

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physical well-being. A possible explanation for this impact, could be that the average age of the patients
is within the ranges of the productive life stage and their perception of inability to fulfill certain activities
that impact their global life proposes.

In this sense, identifying correlates of QoL in different populations of patients with HSCT is an important
first step to identifying individuals at risk for poor QoL or lack of improvement in QoL. Further research
into causal mechanisms will allow for the development of specific psychological interventions to
prevent or control variables found to deteriorate QoL or to increase factors found to improve the QoL
of these patients and contribute to their interdisciplinary care.

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