Generic selectors
Exact matches only
Search in title
Search in content
Filter by Categories
Abstract
Abstracts
Brief Communication
Case Report
Case Series
Commentary
Conference Abstract
Conference Editorial
Conference Proceedings
Current Issue
Editorial
Editorial Commentary
Erratum
General Medicine, Case Report
IAPCONKochi 2019 Conference Proceedings
Letter to Editor
Letter to the Editor
Letters to Editor
Narrative Review
Original Article
Palliative Medicine, Letter to the Editor
Personal Reflection
Perspective
Perspectives
Position Paper
Position Statement
Practitioner Section
Report
REPUBLICATION: Special Article (Guidelines)
Review Article
Short Communication
Special Editorial
Special Review
Systematic Review
View/Download PDF

Translate this page into:

Original Article
26 (
4
); 523-527
doi:
10.4103/IJPC.IJPC_223_19

To assess the Prevalence and Predictors of Cancer-related Fatigue and its Impact on Quality of Life in Advanced Cancer Patients Receiving Palliative Care in a Tertiary Care Hospital: A Cross-sectional Descriptive Study

Address for correspondence: Dr. Rakesh Garg, Room No 139, Fist Floor, Department of Onco-Anaesthesia and Palliative Medicine, Dr. BRAIRCH, All India Institute of Medical Sciences, Ansari Nagar, New Delhi - 110 029, India. E-mail: drrgarg@hotmail.com
Licence

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Disclaimer:
This article was originally published by Wolters Kluwer - Medknow and was migrated to Scientific Scholar after the change of Publisher; therefore Scientific Scholar has no control over the quality or content of this article.

Abstract

Introduction:

Cancer-related fatigue (CRF) is one of the adverse outcomes of cancer and its treatment. Despite its high prevalence; the data are scarce from the Indian population on the prevalence of CRF and its predictors in advanced cancer patients. Hence, we aim to find the prevalence of the fatigue, its impact of fatigue on quality of life (QOL), and possible predictors.

Methods:

This study was conducted after approval of the ethical committee in adult patients of advanced cancer receiving palliative care. The data collected included demographic details, nutritional status, any comorbidities involving cardiorespiratory, renal, pulmonary, and neurological system, type and stage of cancer, site of metastasis, any previous or ongoing chemotherapy or radiotherapy, history of drug intake, hemoglobin, and albumin. The study parameters included assessment of fatigue, QOL, and symptom assessment as per the validated tools. The primary objective of the study was to find the prevalence of fatigue in advanced cancer patients receiving palliative care. The secondary objectives were to find predictive factors of fatigue, its impact on QOL of patients, and the relation between the fatigue and QOL receiving palliative care. The correlation between fatigue score and QOL was analyzed using Pearson's correlation coefficient. Multiple linear regression analysis was performed for identifying the predictors of CRF.

Results:

The fatigue was observed in all 110 patients in this study. Of these, severe fatigue was seen in 97 patients (Functional Assessment of Chronic Illness Therapy [FACIT]-F < 30). The median (interquartile range [IQR]) FACIT-F score was 14 (8–23). The median (IQR) of the overall QOL was 16.66 (16.6–50). The correlation between the fatigue (FACIT-F) and QOL was + 0.64 (P < 0.001). The predictors of fatigue included pain, physical functioning, Eastern Cooperative Oncology Group, tiredness, and the level of albumin.

Conclusion:

We conclude that the prevalence of fatigue in Indian patients with advanced cancer receiving palliative care was high and it has a negative impact on QOL. Pain, physical functioning, performance status, and albumin were found to be independent predictors of CRF.

Keywords

Albumin
cancer
fatigue
pain
palliative care
predictors
quality of life

INTRODUCTION

Cancer-related fatigue (CRF) is one of the adverse outcomes of cancer and its treatment. It has been defined as “a distressing persistent, subjective sense of physical, emotional and/or cognitive tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning.”[1] CRF can occur not only during the start of cancer treatment but may also occur later during the course of treatment. It may continue to persist even after completion of cancer-related treatment.

The pathophysiology of CRF may be due to a dysregulation of the neuroimmunoendocrine system.[2] It includes interaction among various factors such as cytokines and neurotransmitters and modifies hypothalamic–pituitary–adrenal axis and circadian rhythms.[2] CRF has been observed to negatively affect the patients' quality of life (QOL) and activities of daily living.[3] Severe fatigue impairs the QOL physically, mentally, emotionally, socially, and spiritually.[456] There can be many contributing factors of fatigue in cancer patients such as patient demographic characteristics, comorbid conditions, performance status of patients, primary malignancy, intensity and type of treatment, nutritional status, patient reported symptoms such as pain, depression and anxiety, sleep disturbances, nausea, abnormal laboratory values such as anemia, low albumin, electrolyte disturbances, and medications.[789]

The prevalence of CRF in cancer patients receiving treatment varies from 60% to 96%.[10] Despite its high prevalence; the data are scarce from the Indian population on the prevalence of CRF and its predictors in advanced cancer patients. Although various studies have been done in the past to evaluate the fatigue among patients with cancers receiving treatment, very few studies have been done in patients receiving the palliative care. This study might fill up the knowledge gap, and appropriate interventions can be given in the early stage of diagnosis of CRF by identifying its prevalence, the predictors, and thus timely appropriate management which, in turn, would improve the QOL. Hence, we aim to find the prevalence of the fatigue, its impact of fatigue on QOL, and possible predictors of fatigue in patients with advanced cancer receiving palliative care at a tertiary care center.

METHODS

This cross-sectional descriptive study was conducted at the palliative care unit of a tertiary care institute after approval of the institutional ethical committee (IEC) (vide ref no. IEC-666/01.12.2017, RP-25/2017 dated December 19, 2017). The protocol was registered at Clinical Trials Registry-India (CTRI)/2018/01/011189 at CTRI. The study was conducted in compliance with the Declaration of Helsinki and its amendments and was conducted according to the principles of Good Clinical Practice. All patients of >18 years of age with advanced cancer receiving palliative treatment and have been denied curative treatment (medical, surgical, or radiotherapy) with the Eastern Cooperative Oncology Group (ECOG) Performance Status score ranging from 0 to 3 and predicted survival of >4 weeks presenting to the palliative care unit were included in the study. Patients having a history of any psychiatric disorder or inability to communicate were excluded from the study. Patients were explained about the study protocol and written informed consent was obtained.

The data were collected on a standard pro forma which included demographic details; nutritional status; any comorbidities involving cardiorespiratory, renal, pulmonary, and neurological system; type and stage of cancer; site of metastasis; any previous or ongoing chemotherapy or radiotherapy and its details; any history of drug intake such as steroids and analgesics; and blood investigations such as hemoglobin (Hb) and albumin. The data were collected from the patients' history and also from the hospital manual and electronic records. The study parameters included assessment of fatigue, QOL, and symptom assessment as per the following validated tools:

  • Edmonton Symptom Assessment Scale Revised: Assessed the patients symptom including pain, nausea, loss of appetite, dyspnea, sleep disturbances, depression, and anxiety

  • EORTC Quality of Life Questionnaire (QLQ)-Core 15-Palliative module (EORTC QLQ-C15-PAL): Assessed the QOL of the patient. This tool consists of 15 items including a global health status/QOL item, a 5-item functioning subscale (assessing physical, role, emotional, cognitive, and social functioning), and a 9-item symptom subscale (assessing fatigue, nausea and vomiting, pain, dyspnea, insomnia, appetite loss, constipation, diarrhea, and financial difficulties)

  • Functional Assessment of Chronic Illness Therapy (FACIT)-F: Assessed the patient fatigue. This tool is a short, 13-item and easy to administer tool that measures an individual's level of fatigue during their usual daily activities over the past week. The level of fatigue is measured on a 5-point Likert scale (4 = not at all fatigued to 0 = very much fatigued). By scoring convention, after appropriate reverse scoring of 11 items, lower scores on the FACIT-F subscale indicate greater levels of fatigue. The score ranges from 0 to 52. A score of <30 indicates severe fatigue. The higher the score on FACIT-F scale, the better is the QOL.

The patients were provided sets of a questionnaire which contains both English and Hindi version of the questionnaire (which are already validated in either language) as per the understanding of the patient. The patient record sheet was filled with the assistance of the researcher.

The primary objective of the study was to find the prevalence of fatigue in advanced cancer patients receiving palliative care. The secondary objectives were to find the predictive factors of fatigue, its impact on QOL of patients, and the relation between the fatigue and QOL receiving palliative care.

Statistical analysis

In a study by Kapoor A et al., they evaluated CRF using FACIT F scale and reported that the mean ± standard deviation (SD) fatigue score was 36 ± 3.84. Based on these data and assuming the precision of 2% of the fatigue score, the sample size calculated was 108. Thus, we recruited 110 patients for our study.

A statistical analysis was done using IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. (Armonk, NY: IBM Corp.). Mean ± SD and other descriptive analysis of parameters including study tools scores were calculated. The correlation between fatigue score and QOL was analyzed using Pearson's correlation coefficient. Multiple linear regression analysis was performed for identifying the predictors of CRF. Variables with significance levels P < 0.05 continued in the regression model.

RESULTS

We assessed 132 patients for inclusion, but 22 patients were not meeting the inclusion/exclusion criteria. Finally, a total of 110 patients were recruited in the study and demographic profile [Table 1], clinical parameters [Table 2], and Edmonton Symptom Assessment Scale -symptoms [Table 3] was noted. The most common malignancy was gastrointestinal (22.7%) followed by genitourinary (20%). The most common modality of treatment received was chemotherapy (59%).

Table 1: Demographic characteristics
Variable n
Gender (male:female) 47:63
Age (years), mean±SD 46.8±13.77
 <20 4
 21-40 33
 41-60 49
 61-80 24
BMI (mg/m2), mean±SD 20.83±4.76
 <18.5 33
 18.5-24.9 58
 25-29.9 12
 30-39.9 7
Comorbidities
 Hypertension 19
 Diabetes mellitus 14
 Coronary artery disease 3
 COPD 2
 Chronic renal failure 2
 Chronic liver disease 2
 Endocrine 3
 Seizure disorder 1
Site of primary cancer
 Head and neck 13
 Gastrointestinal 25
 Genitourinary 22
 Thoracic 3
 Breast 12
 Lung 15
 Hematological 6
 Bone and soft tissue 3
 PNET 1
 Melanoma 1
 CUP 1
 Miscellaneous 7
Treatment received
 Chemotherapy 65
 Radiotherapy 42
 Chemotherapy + radiotherapy 26
 Surgery 18

COPD: Chronic obstructive pulmonary disease, BMI: Body mass index, SD: Standard deviation, PNET: Primitive neuroectodermal tumor, CUP: Cancer of unknown primary

Table 2: Clinical parameters
Variable n
ECOG, mean±SD 2.4±0.60
 ECOG (1:2:3) 6:46:58
Hb, mean±SD (g/dL) 10±2.2
 Hb (<10) 57
 Hb (>10) 53
Albumin, mean±SD (g/dl) 3.32±0.66
 Albumin (<3.5) 69
 Albumin (>3.5) 41
Daily morphine consumption (mg)
 <30 61
 30-60 15
 60-120 17
 >120 17

Hb: Hemoglobin, SD: Standard deviation, ECOG: Eastern Cooperative Oncology Group

Table 3: Edmonton Symptom Assessment Scale - symptoms
None Mild (1-3) Moderate (4-6) Severe (7-10)
Pain 5 27 42 36
Dyspnea 48 28 12 22
Tiredness 1 14 28 67
Nausea/vomiting 38 41 10 20
Lack of appetite 8 20 23 59
Drowsiness 25 46 22 17
Depression 24 25 27 33
Anxiety 17 29 25 39
Well-being 2 9 26 47
Constipation 70 6 12 22

The median (interquartile range [IQR]) of daily morphine consumption was 30 (15–90) mg. Of the 110 patients, 92 patients were on opioids and the rest were on nonopioid analgesics.

The fatigue was observed in all 110 patients in this study. Of these, severe fatigue was seen in 97 patients (FACIT-F <30). The median (IQR) FACIT-F score was 14 (8–23). The median (IQR) of overall QOL was 16.66 (16.6–50) and other variables of QOL are shown in Table 4.

Table 4: Quality of life
Variable Median (IQR)
Overall QOL 16.66 (16.66-50)
Functional scales
 Physical function 34 (16.66-66.66)
 Emotional function 34 (33.33-67)
Symptom scales
 Dyspnea score 33.33 (0-66.66)
 Pain score 66.66 (50-100)
 Insomnia score 63.66 (33.33-100)
 Fatigue score 66.66 (66.66-100)
 Appetite score 66.66 (33.33-100)
 Nausea/vomiting score 33.33 (0-66.66)
 Constipation score 33.33 (0-66.66)

QOL: Quality of life, IQR: Interquartile range

The correlation between fatigue (FACIT-F) and QOL was + 0.64 (P < 0.001). Similarly, there was a highly significant (P < 0.001) positive correlation between FACIT-F and physical (+0.70) and emotional scores (+0.45) of QOL. The symptom scores (as assessed from EORTC QLQ-C15-PAL) had a highly significant (P < 0.001) negative correlation with FACIT-F except for dyspnea [Table 5]. Among other factors, FACTI-F was found to have significant positive correlation with body mass index (BMI) (P = 0.0008), Hb (P = 0.0002), albumin (P < 0.0001), and negative correlation with ECOG score (<0.0001).

Table 5: Correlation between fatigue (Functional Assessment of Chronic Illness Therapy-F) and quality of life
Variable Ρ P
Overall QOL +0.64 <0.0001
Functional scales
 Physical function +0.70 <0.0001
 Emotional function +0.45 <0.0001
Symptom scales
 Fatigue score −0.72 <0.0001
 Nausea/vomiting score −0.36 0.0001
 Pain score −0.53 <0.0001
 Dyspnea score −0.00 0.95
 Insomnia score −0.44 <0.0001
 Appetite score −0.58 <0.0001
 Constipation score −0.25 0.006

QOL: Quality of life

A linear regression model was constructed with fatigue as dependent variable and QOL variables and other demographic characteristics (age, BMI, comorbidities, and treatment received) and clinical variables (ECOG, Hb, and albumin) as independent variables. The predictors of fatigue included pain, physical functioning, ECOG, tiredness, and level of albumin [Table 6]. On further subgroup analysis, it was found that there was no statistically significant difference between mean FACIT-F scores of different age groups (P > 0.05). The mean FACIT-F scores of group with BMI 25–29.9 (25 ± 8.57) were significantly (P< 0.001) higher as compared to group with BMI <18.5 (13.3 ± 7.78) and those with BMI 18.5–24.9 (15 ± 10.4). The mean FACIT F scores were significantly (P< 0.001) lower in patients with ECOG 3 (10.3 ± 6.4) as compared to ECOG 1 (27.5 ± 7.7) and ECOG 2 (21.6 ± 9.8) patients. Patients with Hb <10 g/dL had significantly (P < 0.001) lower mean FACIT-F scores (12.8 ± 8.5) as compared to those with Hb >10 g/dL (19.4 ± 10.6). The mean FACIT F scores were significantly lower (P< 0.001) in patients with albumin <3.5 g/dL (13.13 ± 8.80) as compared to those with albumin >3.5 g/dL (20.82 ± 10.47).

Table 6: Linear regression model for predictors of fatigue
Variable Coefficient SE P 95% CI
Pain −0.07 0.02 0.001 −0.12-0.03
Physical function 0.08 0.02 0.002 0.03-0.14
ECOG −2.37 1.12 0.03 −4.61-0.13
Tiredness −0.13 0.03 0.000 −0.19-0.07
Albumin 2.15 0.85 0.014 0.45-3.85

SE: Standard error, CI: Confidence interval, ECOG: Eastern Cooperative Oncology Group

DISCUSSION

In our study, we observed a prevalence of 100% among patients of advanced cancer receiving palliative care. Of these, severe fatigue was found in 88.18% of the patients. CRF has a negative impact on QOL. Pain, physical functioning, performance status, and albumin were found to be independent predictors of CRF. These findings could be explained as CRF can carry on for months or even years after the termination of cancer treatment and the patients also had advanced cancer.

CRF is among the most distressing and prevalent symptoms among patients of advanced cancer receiving treatment.[1112] CRF leads to poor QOL and activities of daily living. This leads to poor social interaction and poor job attendance. In our study, FACIT-F had a significant positive correlation with overall QOL, i.e., if FACIT-F decreased (severity of fatigue increased), then QOL also decreased. Fatigue (FACIT-F) also had a significant positive correlation with other variables of EORTC QLQ-C15-PAL such as physical and emotional functioning and negative correlation with symptoms scores of EORTC QLQ-C15-PAL such as pain, lack of appetite, lack of sleep, tiredness, nausea vomiting, and constipation except for dyspnea. Previous studies have also shown that fatigue significantly affects the QOL.[1314151617] In our study, fatigue had a significant correlation with other factors such as BMI, Hb, albumin, and ECOG score. In few studies, anemia has shown to be the predictor of fatigue.[18]

We also found that the independent predictors of CRF were pain (P = 0.001), physical functioning (P = 0.002), ECOG (P = 0.03), tiredness (P < 0.001), and albumin (P = 0.014). Pain has been shown as an important predictor of fatigue in many other studies.[19202122] Low albumin level has been associated with the severity of fatigue in some studies.[132324] Poor performance status has been associated with increasing severity of fatigue in the previous studies.[1225] We found physical functioning as an important predictor of CRF. It has been observed that patients with more fatigue have lesser physical activities which may subsequently lead to physical deconditioning, and this further exacerbates persistence of fatigue.[262728]

Our study found few predictors of CRF, and thus, certain interventions if done will reduce the severity of fatigue. Management of fatigue includes symptom control such as pain, nausea, appetite, dyspnea, and nutritional supplements to improve Hb and albumin and exercises to improve physical functioning. It has been reported that pain leads to increased occurrence of CRF, and the authors concluded that the optimal analgesic management would mitigate CRF.[29] A recent meta-analysis reported that exercise decreases the occurrence of CRF in cancer survivors and more so in person with high adherence to exercise protocol.[3031] It has also been observed that improvement in biochemical parameters such as Hb and albumin leads to amelioration in fatigue.[13] The combination of physical training and increased protein intake has been found to be beneficial, more so in patients with early stage of cachexia as compared to refractory cachexia.[32]

Our study is limited by the fact for evaluation of various interventions to prevent the occurrence of CRF. Although the factors responsible for CRF have been elucidated from our study, the relevant interventions and their outcomes need to be further studied.

CONCLUSION

The prevalence of fatigue in Indian patients with advanced cancer receiving palliative care was high and it has a negative impact on QOL. Pain, physical functioning, performance status, and albumin were found to be independent predictors of CRF.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

REFERENCES

  1. . NCCN Clinical Practice Guidelines in Oncology: Cancer-Related Fatigue, Version 2. Avilable from: https://wwwnccnorg/professionals/physician_gls/defaultaspx
  2. , , . Minireview: Neuro-immuno-endocrine modulation of the hypothalamic-pituitary-adrenal (HPA) axis by gp130 signaling molecules. Endocrinology. 2002;143:1571-4.
    [Google Scholar]
  3. , . Assessment and management of cancer-related fatigue. J Hosp Palliat Nurs. 2013;15:1-6.
    [Google Scholar]
  4. , , . Chronic pain and fatigue: Associations with religion and spirituality. Pain Res Manag. 2008;13:383-8.
    [Google Scholar]
  5. , , . The level of and relation between hope, hopelessness and fatigue in patients and family members in palliative care. Palliat Med. 2005;19:234-40.
    [Google Scholar]
  6. , . Fatigue experience in advanced cancer: A phenomenological approach. Int J Palliat Nurs. 2004;10:15-23.
    [Google Scholar]
  7. , , , , , . Fatigue and its associated factors in ambulatory cancer patients: A preliminary study. J Pain Symptom Manage. 1999;17:42-8.
    [Google Scholar]
  8. , , , , , , . Multidimensional fatigue and its correlates in hospitalised advanced cancer patients. Eur J Cancer. 2007;43:1030-6.
    [Google Scholar]
  9. , , , , , , . Nausea and disturbed sleep as predictors of cancer-related fatigue in breast cancer patients: A multicenter NCORP study. Support Care Cancer. 2017;25:1271-8.
    [Google Scholar]
  10. , , , . Fatigue in patients with cancer. Eur J Cancer. 1998;34:1670-6.
    [Google Scholar]
  11. , , , , , . Cancer-related fatigue: Evolving concepts in evaluation and treatment. Cancer. 2003;98:1786-801.
    [Google Scholar]
  12. , . The impact of fatigue on patients with cancer: Overview of fatigue 1 and 2. Oncologist. 2000;9:125-7.
    [Google Scholar]
  13. , , , . Association of cancer-related fatigue with other symptoms and impact on quality of life of palliative care patients in a tertiary cancer institute: A prospective observational study. J Pain Symptom Manage. 2016;51:435-41.
    [Google Scholar]
  14. , , , , , , . Assessment of cancer-related fatigue, pain, and quality of life in cancer patients at palliative care team referral: A multicenter observational study (JORTC PAL-09) PLoS One. 2015;10:e0134022.
    [Google Scholar]
  15. , , , , , . Prevalence of fatigue among cancer patients receiving various anticancer therapies and its impact on quality of life: A cross-sectional study. Indian J Palliat Care. 2012;18:165-75.
    [Google Scholar]
  16. , , . Cancer related fatigue and quality of life in patients with advanced prostate cancer undergoing chemotherapy. Biomed Res Int. 2016;3989286:1-11.
    [Google Scholar]
  17. , , , . The relationship between cancer-related fatigue and patient satisfaction with quality of life in cancer. J Pain Symptom Manage. 2007;34:40-7.
    [Google Scholar]
  18. , , , , . Anaemia and other predictors of fatigue among patients on palliative therapy for advanced cancer. Anticancer Res. 2009;29:2569-75.
    [Google Scholar]
  19. , , , . Concerns about breast cancer, pain, and fatigue in non-metastatic breast cancer patients undergoing primary treatment. Healthcare (Basel). 2016;4:62.
    [Google Scholar]
  20. , , , , , . Predictors of fatigue in cancer patients before and after chemotherapy. J Health Psychol. 2014;19:699-710.
    [Google Scholar]
  21. , , , , . Comprehensive predictors of fatigue for cancer patients. Taehan Kanho Hakhoe Chi. 2006;36:1224-31.
    [Google Scholar]
  22. , , , , . Multidimensional independent predictors of cancer-related fatigue. J Pain Symptom Manage. 2003;26:604-14.
    [Google Scholar]
  23. , , , . Factors associated with the severity and improvement of fatigue in patients with advanced cancer presenting to an outpatient palliative care clinic. BMC Palliat Care. 2012;11:16.
    [Google Scholar]
  24. , , , , , , . Clinical factors associated with cancer-related fatigue in patients being treated for leukemia and non-Hodgkin's lymphoma. J Clin Oncol. 2002;20:1319-28.
    [Google Scholar]
  25. , , . Predictors of fatigue and quality of life in a prospective palliative care cohort. J Clin Oncol. 2006;18:8571-81.
    [Google Scholar]
  26. , . Patterns of fatigue and activity and rest during adjuvant breast cancer chemotherapy. Oncol Nurs Forum. 1998;25:51-62.
    [Google Scholar]
  27. , , , , . Strength, physical activity, and age predict fatigue in older breast cancer survivors. Oncol Nurs Forum. 2008;35:815-21.
    [Google Scholar]
  28. , , , , , . Cardiorespiratory and neuromuscular deconditioning in fatigued and non-fatigued breast cancer survivors. Support Care Cancer. 2013;21:873-81.
    [Google Scholar]
  29. , , , , , , . Time course and predictors for cancer-related fatigue in a series of oropharyngeal cancer patients treated with chemoradiation therapy. Oncologist. 2012;17:569-76.
    [Google Scholar]
  30. , , , . The effect of exercise on cancer-related fatigue in cancer survivors: A systematic review and meta-analysis. Neuropsychiatr Dis Treat. 2018;14:479-94.
    [Google Scholar]
  31. , , , , , , . Effects of aerobic and resistance exercises on physical symptoms in cancer patients: A meta-analysis. Integr Cancer Ther. 2018;17:1048-58.
    [Google Scholar]
  32. , , . Muscle protein anabolism in advanced cancer patients: Response to protein and amino acids support, and to physical activity. Ann Oncol. 2018;29:1110-7.
    [Google Scholar]
Show Sections