The Nethersole college of Nursing, Faculty the Medicine, The Chinese university of Hong Kong, Hong Kong SAR, China

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Winnie K. W. So, 7/F Esther Lee Building, The Nethersole school of Nursing, Faculty the Medicine, The Chinese college of Hong Kong, Shatin, the brand-new Territories, Hong Kong SAR, China.

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The Nethersole institution of Nursing, Faculty that Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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The Nethersole college of Nursing, Faculty of Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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The Nethersole school of Nursing, Faculty that Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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The Nethersole institution of Nursing, Faculty of Medicine, The Chinese university of Hong Kong, Hong Kong SAR, China

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The Nethersole school of Nursing, Faculty of Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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School of Nursing, Midwifery and Social Work, university of Queensland and Mater wellness Services, Brisbane, Queensland, Australia

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The Nethersole college of Nursing, Faculty that Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

Correspondence

Winnie K. W. So, 7/F Esther Lee Building, The Nethersole school of Nursing, Faculty the Medicine, The Chinese college of Hong Kong, Shatin, the new Territories, Hong Kong SAR, China.

The Nethersole institution of Nursing, Faculty of Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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The Nethersole institution of Nursing, Faculty of Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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The Nethersole institution of Nursing, Faculty that Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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The Nethersole institution of Nursing, Faculty that Medicine, The Chinese university of Hong Kong, Hong Kong SAR, China

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The Nethersole institution of Nursing, Faculty that Medicine, The Chinese college of Hong Kong, Hong Kong SAR, China

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School the Nursing, Midwifery and Social Work, university of Queensland and also Mater health Services, Brisbane, Queensland, Australia

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Breast cancer patients often experience symptoms the adversely influence their top quality of life. The is construed that plenty of of this symptoms have tendency to swarm together: if they could have different manifestations and occur during various phases of the condition trajectory, the symptoms regularly have a typical aetiology the is a potential target for intervention. Knowledge the symptom clusters associated with breast cancer might usefully inform the breakthrough of effective care plans for affected patients. The aim of this file is to administer an update systematic evaluation of the well-known symptom clusters among breast cancer patients during and/or ~ cancer treatment. A search was carried out using five databases for researches reporting symptom clusters among breast cancer patients. The search gave in 32 studies for inclusion. The findings suggest that fatigue-sleep disturbance and also psychological symptom swarm (including anxiety, depression, nervousness, irritability, sadness, worry) are the many commonly-reported symptom clusters among breast cancer patients. Further, the composition of symptom clusters often tends to change across various step of cancer treatment. When this review identified some commonalities, the different methodologies supplied to recognize symptom clusters brought about inconsistencies in symptom cluster identification. It would be advantageous if future studies can separately study the symptom swarm that take place in breast cancer patient undergoing a certain treatment type, and also use standardised instruments across studies to assess symptoms. The testimonial concludes that more studies could usefully recognize the organic pathways connected with assorted symptom clusters, i beg your pardon would inform the breakthrough of effective and also efficient symptom management strategies.


1 INTRODUCTION

Breast cancer is among the most prevalent cancers worldwide, and also patients regularly experience unpleasant symptoms during their treatment which adversely influence their high quality of life (QOL).1 Previous research study on the symptoms knowledgeable by cancer patients has revealed the cancer-associated symptoms frequently do not happen in isolation, and they can have a usual or related aetiology, meaning that one symptom can influence the occurrence and also severity that other, frequently related, symptoms. Therefore, research has been directed in the direction of the expedition of teams of associated cancer-associated symptoms that take place concurrently among patients throughout treatment. The exploration of this symptom groups, formally defined as ‘symptom clusters’ by Kim et al.,2 provides helpful clues for the advancement of techniques for symptom management, through which symptoms may be controlled simultaneously through a single intervention. This strategy could aid save resources and also reduce healthcare providers’ prices in caring for cancer patients. Much better understanding of symptom clusters amongst cancer patients could likewise enhance the quality of care detailed to influenced individuals, enabling greater QOL.

Despite the increasing variety of studies exploring and identifying symptom clusters skilled by breast cancer patient both during and after treatment, few published organized reviews have summarised the result to educate practice. Back Dong et al.3 carried out a systematic review on symptom clusters determined in patient with assorted cancer types, this evaluation only consisted of studies in i beg your pardon the participants were patients with progressed cancer. Research studies identifying symptom clusters amongst early stage and also non-metastatic breast cancer patients were no included. Nguyen et al.4 additionally conducted a literary works review on symptom clusters amongst breast cancer patients. However, the writer did not examine the longitudinal alters in symptom clusters patient report at miscellaneous stages that the therapy trajectory. That is known, however, that symptom occurrence and severity can readjust during this trajectory.5 A summary of how symptom clusters could evolve over the course of treatment amongst breast cancer patients is thus required to carry out insights into just how symptom management strategies for cancer patients could best be tailored come each therapy stage.

The objective of this testimonial is to carry out an updated overview of the determined symptom clusters skilled by breast cancer patients throughout and/or ~ cancer treatment. The testimonial is guided by two questions. In patients treated for breast cancer: (1) What symptom clusters take place before, during and also after cancer treatment; and (2) execute the compositions the the symptom clusters, identified as the number and types of symptoms within the symptom clusters, change during cancer treatment?

2 METHODS

2.1 find strategy

A literary works search was conducted in may 2020. 5 databases were provided in the search, specific OVID MEDLINE, PubMed, EMBASE, PsycINFO and also CINAHL, to identify published research studies that met the eligibility criteria of the review, as set out below. A hands-on search making use of Google Scholar was additionally conducted come identify additional eligible studies. The search strategy used for this evaluation was together follows: ‘breast cancer’ OR ‘breast carcinoma’ OR ‘breast tumour’ OR ‘breast malignancy’ and also ‘symptom cluster’ OR ‘symptom clusters’ OR ‘multiple symptoms’ OR ‘symptom constellations’ OR ‘concurrent symptoms’ OR ‘co-occurring symptoms’.

2.2 Eligibility criteria

Studies eligible because that inclusion in the evaluation were initial studies of any study style that report the to know of one or an ext symptom clusters in ~ a single group of breast cancer patients at any type of stage in your cancer therapy trajectory. Any articles the were not initial articles, or those that did not recognize breast cancer-associated symptom clusters, were excluded. Posts that to be not published in English were likewise excluded. Moreover, together the concept of symptom swarm in oncology was first introduced in 2001,6 we limited the consist of of articles to those released in or after ~ January 2001.

2.3 Data extraction

After the literature search, the titles and abstracts the the identified articles were an initial independently screened by 2 authors follow to the eligibility criteria. The complete text of write-ups deemed standard on screening to be then examined to completely verify consist of in this review. Any disagreements ~ above eligibility were resolved by discussion in between the 2 authors.

Data extraction was then independently conducted by 2 authors native the default studies. The extracted data made up study settings, research design, sample size, the methodologies offered in symptom cluster identification, the symptom clusters identified, the symptoms in every cluster and the instruments used for symptom assessment in the studies.

To evaluate the security of symptom clusters end time, data were gathered on the symptoms in the determined symptom clusters at various time points during the longitudinal studies. Differences in the compositions of this symptom clusters across time were identified by compare the numbers and types of symptoms involved in this clusters at assorted time points. The existence of much less than 75% the the symptom in a specific symptom swarm at each time suggest of symptom assessment suggest the instability the the symptom swarm over time.7 Furthermore, a symptom cluster had actually to be present at all time point out of the assessment for it to be considered stable.

As the outcomes that the consisted of studies on symptom cluster identification generally did not contain quantitative data, and the qualities of the participants involved in the included studies, such together the treatment received, to be heterogeneous, a meta-analysis was not performed. The testimonial findings space presented narratively in a tabular manner.

2.4 Reporting top quality assessment of the consisted of studies

The top quality of study reporting in the consisted of studies was appraised using the 14-item Standard top quality Assessment Criteria for evaluating Primary Research records from a range of Fields arisen by Kmet et al.8 This quality assessment tool has previously been used for critical appraisal of studies in methodical reviews that observational studies9 and randomised regulated trials.10 The items provided for assessing the quality of the researches are detailed in Table1. Several of the items native the checklist were no applicable come assessing studies concentrated on symptom-cluster identification, thus studies utilise methodologies of a descriptive or exploratory nature.11 In the assessment, studies were awarded two points because that each item that was fully achieved, and one point for partial achievement of one item. Zero point out were provided for each item the the assessed research studies failed come achieve. The complete score to be then calculated by summing the point out awarded because that each the the applicable items, and also the portion score to be presented. The quality of the assessed researches was climate categorised as minimal (80%), as shown by Lee et al.12 research studies of limited quality to be excluded native the review.


TABLE 1. Items had in the crucial appraisal of the included studies
Item summary of article Item utilised in crucial appraisal?
1 Research inquiries or goals are sufficiently described Yes
2 Study architecture is evident and appropriate Yes
3 Method of subject / compare group selection or source of information / input variables are described and also appropriate Yes
4 Subject qualities are sufficiently described Yes
5 Procedures of random allocation space described Partiallya a Items the are just applicable to studies with a randomized regulated trial design, not included those involving secondary analysis that randomized regulated trials.
6 Procedures that blinding the investigators room described Partiallya a Items that are just applicable to studies with a randomized controlled trial design, not included those involving an additional analysis of randomized managed trials.
7 Procedures the blinding the subjects space described Partiallya a Items the are just applicable to studies with a randomized managed trial design, excluding those involving secondary analysis the randomized managed trials.
8 Outcome and also exposure procedures are fine defined and robust to measurement or misclassification bias, and method of outcome assessment are described Yes
9 Sample dimension utilised in the study is appropriate Partiallya a Items that are just applicable to research studies with a randomized regulated trial design, excluding those involving an additional analysis of randomized regulated trials.
10 Analytical techniques employed room justified and appropriate Yes
11 Estimates the variance space reported in the outcomes section Yes
12 Confounding determinants are managed for Partiallyb b items that are not applicable to research studies utilizing methodologies that room of a descriptive or exploratory nature.
13 Results room reported in adequate detail Yes
14 Conclusions drawn are sustained by the results Yes

a Items that are only applicable to researches with a randomized regulated trial design, excluding those involving second analysis that randomized regulated trials. b items that are not applicable to researches utilizing methodologies that room of a descriptive or exploratory nature.

The reporting top quality was very first assessed by one reviewer, and the assessment outcomes were then separately verified by a 2nd reviewer. Any kind of disagreements in the assessment results created by the 2 reviewers were solved through discussion.

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3 RESULTS

3.1 search results

A total of 626 posts were initially figured out through the literature search that the five databases. Moreover, with our hands-on search, one additional original article was identified and also determined to accomplish the eligibility criteria. Duplicated articles (n=318), articles that were no original write-ups published in English (n=125), and also those that were published prior to January 2001 (n=13) were then removed. The summary of the continuing to be 170 write-ups were screened to identify studies that reported the to know of symptom clusters competent by a team of breast cancer patients. The exemption of 139 short articles reporting research studies that did no fulfil this criterion left a complete of 32 research studies for consist of in this review. The consists of this 32 researches was confirmed by a 2nd author. Every one of the included studies attained a reporting high quality score the at the very least 11 (a portion score that 61%), and therefore none of the studies was to exclude, on the communication of short reporting high quality (Table2). Percentage commitment of the reporting high quality assessment ratings was 91%, where arguments in ratings between the 2 authors affiliated in the conduction of an important appraisal were solved through discussion. Figure1 gives the preferred Reporting item for systematic Reviews and Meta-Analyses (PRISMA) flow diagram that presents the outcomes of the literature search.


TABLE 2. The outcomes of the top quality assessment the the included studies
Author/year items 1 items 2 items 3 item 4 item 5 items 6 article 7 article 8 item 9 items 10 item 11 article 12 items 13 article 14 quality score (% score)
Albusoul et al. (2017) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Alkathiri and Albothi (2015) 2 2 1 2 NA NA NA 1 NA 1 2 NA 1 1 13 (72%)
Bender et al. (2005) 2 1 0 2 NA NA NA 2 NA 2 0 NA 1 1 11 (61%)
Berger et al. (2018) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Bower et al. (2011) 2 0 1 2 NA NA NA 2 NA 2 2 2 2 2 17 (85%)
Browall et al. (2017) 2 0 1 2 NA NA NA 2 NA 2 0 NA 1 1 11 (61%)
Chongkham-ang et al. (2018) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Chow et al. (2019) 2 1 0 2 NA NA NA 2 NA 2 2 NA 2 0 13 (72%)
Evangelista and also Santos (2012) 2 1 2 2 NA NA NA 2 NA 2 0 NA 2 2 15 (83%)
Fu et al. (2009) 2 0 2 2 NA NA NA 1 NA 2 2 NA 2 2 15 (83%)
Glaus et al. (2006) 2 2 1 2 NA NA NA 2 NA 2 2 2 2 2 19 (95%)
Hsu et al. (2017) 2 2 2 2 NA NA NA 2 NA 2 2 0 2 1 17 (85%)
Kenne Sarenmalm et al. (2014) 2 2 2 2 NA NA NA 2 NA 2 2 NA 2 2 18 (100%)
Khan et al. (2018) 1 1 1 2 NA NA NA 1 NA 2 1 NA 1 2 12 (67%)
Kim et al. (2008) 2 2 2 2 NA NA NA 2 NA 2 2 NA 2 2 18 (100%)
Lengacher et al. (2012) 2 2 1 2 1 0 0 2 1 2 2 1 2 2 20 (71%)
Li et al. (2019) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Li et al. (2020) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Marshall et al. (2016) 2 2 1 2 NA NA NA 1 NA 2 0 NA 2 2 14 (78%)
Matthews et al. (2012) 2 2 1 2 NA NA NA 1 NA 2 2 NA 2 2 16 (89%)
Mazor et al. (2018) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Nho et al. (2018) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Phligbua et al. (2013) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Reich et al. (2017) 2 2 1 2 1 0 0 2 2 2 2 2 2 2 22 (79%)
Roiland and Heidrich (2011) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Savard et al. (2011) 2 2 1 2 NA NA NA 2 NA 2 2 0 2 2 17 (85%)
Starkweather et al. (2017) 1 2 1 2 NA NA NA 2 NA 2 2 NA 1 1 14 (78%)
Suwisith et al. (2008) 2 2 2 2 NA NA NA 2 NA 1 2 NA 2 2 17 (94%)
Uysal et al. (2018) 2 1 1 2 NA NA NA 2 NA 2 2 NA 1 0 13 (72%)
Ward Sullivan et al. (2017) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Ward Sullivan et al. (2018) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)
Wiggenraad et al. (2020) 2 2 1 2 NA NA NA 2 NA 2 2 NA 2 2 17 (94%)

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3.2 examine characteristics

The features of the 32 consisted of studies are presented in Table3. Inter-rater arguments in the extract data emerged on 12 items displayed in the table during data extraction, and also these were solved through discussion. The contained studies were published between 2005 and also 2020. Of these 32 studies, 13 to be cross-sectional,13-25 11 to be longitudinal,26-36 if the remaining eight associated a randomised clinical trial design.37-44 among these included studies, 16 associated the an additional analysis the the data of present studies,14, 42-44 the which six were observational research studies involving second analysis that data indigenous randomised clinical trials.37-39, 42-44 Eleven of the had studies (34%) presented longitudinal changes in the composition of symptom clusters experienced by patient before, during and/or after ~ cancer treatment.27, 44 One study involved a pooled, second data evaluation of 3 previous studies including participants at various stages of cancer treatment.20


Author/year/country Study architecture Patient characteristics/sample size Methodology that symptom swarm identification instruments used for symptom assessment
Albusoul et al. (2017); USA Secondary data analysis of a randomised managed trial Stage ns to IIIA breast cancer patient receiving adjuvant chemotherapy (N=178-202) Exploratory aspect analysis

Hospital Anxiety and Depression scale Symptom Experience range

Alkathiri and also Albothi (2015); Saudi Arabia Cross-sectional study Stage i to IIIA chest cancer patients receiving chemotherapy (N=100) Not specified

Symptom Experience range

Bender et al. (2005); USA (Study 1)a a Bender et al. (2005) study consists of three independent research studies using three different samples that participants. Secondary data evaluation of a cross-sectional study Stage 0 come II chest cancer patients that completed surgery and also before starting adjuvant chemotherapy (N=40) Hierarchical swarm analysis

file of Mood claims Symptom Checklist The Kupperman index The everyday Symptom Diary

Bender et al. (2005); USA (Study 2)a a Bender et al. (2005) study consists of three independent research studies using three different samples that participants. Stage i to III chest cancer patients that completed adjuvant chemotherapy (N=88)
Bender et al. (2005); USA (Study 3)a a Bender et al. (2005) study consists of three independent studies using three various samples of participants.

Stage IV (metastatic) chest cancer patients v mild anaemia (N=26)

Patients were either receiving palliative chemotherapy or had actually completed chemotherapy therapy in the past

Berger et al. (2020); USA Secondary data analysis of a randomised regulated trial breast cancer patient receiving surgery and chemotherapy, cancer stages not specified (N=202) Exploratory variable analysis

Hospital anxiety and depression range Symptom experience range

Bower et al. (2011); USA Secondary data analysis of a cross-sectional study Stage 0 to IIIA breast cancer patients receiving chemotherapy and/or radiotherapy (N=103) Not specified

fatigue symptom inventory Beck depression inventory-II Pittsburgh Sleep top quality Index

Browall et al. (2017); Sweden Secondary data analysis of a randomised controlled trial Stage i to IIIA chest cancer patients receiving chemotherapy (N=124) Principal component analysis

Memorial Symptom Assessment range

Chongkham-ang, et al. (2018); Thailand Cross-sectional study Stage ns to III chest cancer patient receiving chemotherapy (N=322) Exploratory factor analysis with principal component analysis

Thai Memorial Symptom Assessment scale

Chow et al. (2019); Canada Longitudinal study Stage 0 come IV chest cancer patient receiving radiotherapy (N=1224) Principal component analysis, Exploratory factor analysis and hierarchical cluster analysis

Edmonton Symptom Assessment range

Evangelista and also Santos (2012); Brazil Cross-sectional study Stage 0 to IV chest cancer patient completed adjuvant chemotherapy and/or receiving hormone treatment (N=138) Principal component analysis

file of Mood claims EORTC-QLQ-C30 EORTC-BR23

Fu et al. (2009); USA Cross-sectional study Stage 0 come III breast cancer patients completed chemotherapy, radiotherapy or hormonal treatment (N=139) Exploratory aspect analysis

Memorial symptoms Assessment range short type

Glaus et al. (2006); Switzerland Cross-sectional study Breast cancer patient receiving hormonal therapy (cancer stage not specified) (N=373) Hierarchical swarm analysis

Clinical checklist for patients v endocrine treatment IBCSG/Linear Analogue scales (LASA) addressing side effects of hormone treatment and coping with condition and therapy

Hsu et al. (2017); Taiwan Longitudinal study Stage 0 come III breast cancer patient receiving chemotherapy (N=103) Latent class growth analysis

M. D. Anderson Symptom inventory (Taiwan version)

Kenne Sarenmalm et al. (2014); Sweden Secondary data analysis of a longitudinal study Breast cancer patient receiving adjuvant chemotherapy or radiotherapy or palliative therapy (cancer stage not specified) (N=206) Principal ingredient analysis

Memorial Symptom Assessment range

Khan et al. (2018); Bangladesh Cross-sectional study Breast cancer patients, cancer stage and treatment got were not stated (N=112) Hierarchical cluster analysis

Symptoms figured out through examinations in ~ hospitals and also documented in case sheets

Kim et al. (2008); USA Secondary data evaluation of a randomised managed trial Stage 0 come IV chest cancer patient receiving chemotherapy and/or radiotherapy (N=282) Common variable analysis

General tiredness Scale profile of mood claims Pittsburgh Sleep high quality Index Side impact checklist

Lengacher et al. (2012); USA Randomised controlled trial Stage 0 come III chest cancer patient completed chemotherapy and/or radiotherapy (N=82) Hierarchical cluster analysis

M.D. Anderson Symptom perform

Li et al. (2019); USA Secondary data analysis of a longitudinal study Stage i to IIIA breast cancer patients receiving surgical procedure with or there is no chemotherapy (N=339) Exploratory factor analysis

chest Cancer prevention Trial Symptom Checklist file of the atmosphere states quick pain inventory-short kind Beck Depression Inventory-II Patient"s evaluate of very own functioning

Li et al. (2020); USA Secondary data analysis of a longitudinal study Stage ns to IIIA chest cancer patient receiving surgery with or there is no chemotherapy (N=354) Exploratory aspect analysis

chest cancer prevention trial symptom checklist profile of mood claims Beck depression inventory-II Patient"s assessment of own functioning

Marshall et al. (2016); USA Secondary data analysis of a cross-sectional study

Data from MedHelp.org breast cancer forum: breast cancer patient completed cancer treatment (treatment no specified) (N=12,991)

Data from study study: stage I come III chest cancer patients completed chemotherapy or radiotherapy (N=653)

K-medoid clustering

Symptom checklist acquired from the Women"s health and wellness Initiative (used in symptom assessment in the research study study only)

Matthews et al. (2012); USA Secondary data evaluation of a cross-sectional study Stage i to IV chest cancer patients receiving radiotherapy (N=93) Confirmatory factor analysis

Symptom Distress range

Mazor et al. (2018); USA Secondary data analysis of a longitudinal study Stage 0 come IV breast cancer patients receiving surgical procedure (N=398) Exploratory variable analysis

Self-administered comorbidity questionnaire Menopausal Symptoms scale

Nho et al. (2018); south Korea Cross-sectional study Stage 0 to IV breast cancer patient completed surgery, chemotherapy, radiotherapy and/or hormone treatment (N=241) Principal ingredient analysis

EORTC QLQ-C30 EORTC QLQ-BR23 Hospital Anxiety and also Depression range

Phligbua et al. (2013); Thailand Longitudinal study Stage i to IIIA chest cancer patients receiving chemotherapy (N=112) Exploratory factor analysis

The modified Memorial Symptom Assessment scale

Reich et al. (2017); USA Randomised controlled trial Stage 0 to III chest cancer patients completed chemotherapy and/or radiotherapy (N=299) Exploratory aspect analysis

The center for Epidemiological research studies Depression scale State-trait tension inventory viewed Stress scale M.D. Anderson Symptom perform Pittsburgh Sleep quality Index fatigue Symptom Inventory quick pain list

Roiland and also Heidrich (2011); USA Secondary data analysis of a randomised controlled trial Breast cancer patients completed chemotherapy, radiotherapy or hormonal treatment (cancer phase not specified) (N=192) Exploratory factor evaluation and confirmatory variable analysis

Symptom bother Scale–Revised

Savard et al. (2011); Canada Longitudinal study Stage ns to III breast cancer patient receiving chemotherapy and/or radiotherapy (N=58) Canonical correlation analysis

Insomnia Severity Index warm flush diary

Starkweather et al. (2017); USA Longitudinal study Stage i to IIIA breast cancer patient receiving adjuvant chemotherapy (N=75) Exploratory element analysis

Hospital Anxiety and Depression Scale brief fatigue inventory basic Sleep Disturbance Scale quick pain inventory viewed Stress scale CNS an important signs™ (software for assessing cognition)

Suwisith et al. (2010); Thailand Cross-sectional study Stage ns to IV breast cancer patients receiving chemotherapy (N=320) Not specified

Memorial symptoms Assessment range

Uysal et al. (2019); Turkey Cross-sectional study Stage ns to IV breast cancer patients completed surgical procedure and/or receiving chemotherapy (N=170) Hierarchical clustering analysis

Memorial Symptom Assessment scale

Ward Sullivan et al. (2017); USA Secondary data evaluation of a longitudinal study Breast cancer patient receiving adjuvant chemotherapy (cancer phase not specified) (N=515) Exploratory variable analysis

Memorial Symptom Assessment range

Ward Sullivan et al. (2018); USA Secondary data analysis of a longitudinal study Breast cancer patient receiving chemotherapy, cancer stage not stated (N=540) Exploratory variable analysis

Memorial Symptom Assessment scale

Wiggenraad et al. (2020); Sweden Secondary data evaluation of a randomised controlled trial Stage i to IIIA chest cancer patients receiving chemotherapy (N=206) Principal ingredient analysis

Memorial Symptom Assessment scale