|Year : 2019 | Volume
| Issue : 1 | Page : 17-25
Social support and mental health in patients with hematological diseases: The moderating role of insomnia
Karin Hogberg1, Anders Brostrom2
1 Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, Sweden
2 Department of Nursing, School of Health and Welfare, Jönköping University, Jönköping, Sweden
|Date of Web Publication||29-Jan-2019|
Dr. Anders Brostrom
Department of Nursing, School of Health and Welfare, Jönköping University, Jönköping
Source of Support: None, Conflict of Interest: None
Introduction: Patients with hematological diseases (HDs) experience a variety of physiological and psychological symptoms. The purpose was to examine how anxiety, depressive symptoms, insomnia, and mastery are associated with perceived social support and mental health in patients with HDs. Methods: In this cross-sectional study, a convenience sample of 120 patients with HDs participated at a Swedish University hospital. Anxiety and depressive symptoms were measured by the Hospital Anxiety and Depression Scale. Perceived social support, mental health, insomnia, and mastery were measured using Medical Outcomes Study Social Support Survey, the Mental Component Score from the Short-Form Health Survey-12, and Minimal Insomnia Symptoms Scale and Pearlin Mastery Scale, respectively. Structural equation modeling was performed to analyze the data. Results: Associations between depressive symptoms and perceived social support differed depending on the insomnia symptom scores. Conclusion: Health-care personnel should routinely assess not only patients' levels of anxiety and depressive symptoms but also their insomnia to identify areas suitable for interventions to improve social support, as well as patient's mental health.
Keywords: Hematological diseases, mental health, moderation, sleep disorder, social support
|How to cite this article:|
Hogberg K, Brostrom A. Social support and mental health in patients with hematological diseases: The moderating role of insomnia. Soc Health Behav 2019;2:17-25
|How to cite this URL:|
Hogberg K, Brostrom A. Social support and mental health in patients with hematological diseases: The moderating role of insomnia. Soc Health Behav [serial online] 2019 [cited 2020 Feb 22];2:17-25. Available from: http://www.shbonweb.com/text.asp?2019/2/1/17/250998
| Introduction|| |
Hematological diseases (HDs) are disorders which primarily affect the blood. A high number of malignant (i.e., blood cancers) and benign blood diseases (e.g., anemia and hemorrhagic conditions) are included. Patients with HDs often have to go through complex and lengthy treatments with long periods of uncertainty. Studies have shown that psychological distress occurs in up to 50% of patients with HDs.,, Furthermore, insomnia is highlighted as a bothersome symptom for patients with HDs., Insomnia is a sleeping disorder wherein there is a failure to either fall asleep or stay asleep as long as wanted. Insomnia is a rather complex variable to interpret as it might be both a cause and a consequence to psychological distress, anxiety, and depression.
One way to theoretically understand how patients with HDs handle their highly complex illness situation is to consider the appraisal of a stressful situation as an evaluation process consisting of the interplay between inner resources and environmental circumstances, which determines the emotions/“health” that arise., Describe the individual's (i.e., the patient's) inner resources in terms of the personality characteristics: self-esteem, self-denigration, and mastery as important. The sense of mastery is defined as the extent to which one believes life changes are under one's control or if they are fatalistically ruled. Furthermore, environmental circumstances can refer to external support, for example, support from family or professionals. How support is related to health can be explained by its either mitigating the stress appraisal or alleviating the emotions that arise.
In accordance to this theoretical reasoning, external support like nursing interventions within cancer care often consists of information and emotional support with the objective to strengthen the patients' sense of mastery and thus relieve psychological distress. That being in control and having access to support is positive for the emotional outcome has been confirmed in a few studies among patients with HDs., Nevertheless, research claims that patients affected by HDs experience unmet psychosocial needs or a lack of this type of support.,, Programs offering support delivered by web-based counseling service platforms have therefore been developed and implemented.,, An increased access to professional information and emotional support, including the possibility to communicate own perceived problems, might strengthen the patients' psychological resources, and the ability to master stressful situations, and thus relieve stressful symptoms of ill health (e.g., anxiety, depression, and insomnia).
It is commonly known that patients with HDs experience a variety of physiological and psychological symptoms. A patient with a malignant blood disease might except for his/her symptoms associated to the HD also perceive depressive symptoms and sleep problems (e.g., insomnia) as an additional stressor. It is well-known from research that insomnia can contribute to both poor quality of life and depressive symptoms. In reverse to see mastery and support as constant, we hypothesize that the mentioned stressors can possibly have negative effects on the patient's mastery (i.e., causing decreased mastery) and need of support (i.e., increased need of support), as well as on his/her overall mental health (i.e., decreased mental health if mastery and support is lacking). There is, however, to the best of our knowledge, no studies investigating these associations in patients with HDs. An understanding of how anxiety, depressive symptoms, insomnia, and mastery are associated to perceived social support and mental health is of great importance. This knowledge could in clinical practice help nurses to identify patients in need of extra social support and research be used as a foundation to further develop, for example, web-based counseling platforms for patients with HDs. The purpose of this study was therefore to examine how anxiety, depressive symptoms, insomnia, and mastery are associated with perceived social support and mental health in patients with HDs.
| Methods|| |
Design, setting, and sample
This cross-sectional study was performed in a hematology clinic at a University Hospital in Sweden consisting of an outpatient facility and inpatient ward. The clinic's structure is composed of multi-professional diagnosis-related teams addressing the respective diagnoses of lymphoma, leukemia, myeloma, myelodysplastic syndrome/polycythemia vera/anemia, and recipients of stem cell transplants. Structured psychosocial support resources are also linked to the clinic, meaning that a social worker is available for emotional counseling and social/financial advice. In addition, there is a central hospital chapel and information is also given on nonprofit support organizations' resources.
A convenience sample of 120 patients with HDs enrolled at the current clinic was asked about participation in this study during 2012–2014. A research nurse connected to the project invited all eligible patients who visited the clinic to participate. Inclusion criteria were (i) verified and treated HD, (ii) at least 18 years of age, and (iii) understanding of the Swedish language. Participants were asked to fill in questionnaires in connection to the hospital visit. The questionnaires were personally collected by the research nurse.
All participants were informed about the study's purpose, and that participation was voluntary. Ethical approval was obtained from the Regional Ethics Review Board (reg. 549-10).
Patient and disease characteristics
Data regarding age, sex, marital status, and education level were collected from the patient during face-to-face interviews performed by the research nurse. Diagnosis, duration of disease (i.e., months), and aim of treatment (i.e., curative, chronic maintenance, or palliative treatment), were collected from medical records. The categorization of the aim of treatment was based on a consensus agreement between the nurse in charge of the study and an experienced physician specialized on HDs who had knowledge of the specific treatment situation for each patient.
Hospital Anxiety and Depression Scale
The Hospital Anxiety and Depression Scale (HADS) is a well validated and frequently used 14-item self-reported scale that assesses anxiety and depressive symptoms with seven items with four response alternatives (0–3), respectively. This yields a total score for anxiety or depression ranging between 0 and 21, with higher scores indicating higher symptom levels. Higher scores indicate higher symptom levels. For each subscale, a score of 7 or below is regarded as the normal range, and 8–10 indicates a risk of disorder; above 11 indicates the probable presence of a mood disorder. The Swedish version of the HADS has been found to be valid and reliable. Cronbach's α for the anxiety and depression subscales were in the present study 0.84 and 083, respectively. In the present study, the scores from the anxiety and depression subscales were used as continuous variables.
Mental component summary based on the short-form health survey
The Short-Form Health Survey (SF-12) is a widely used and well-validated generic health status questionnaire producing two summary scores; a physical component score and a mental component score (MCS). In this study, only the MCS was used, where 6 items summarize mental health, role limitations due to emotional problems, social functioning, and vitality. A higher score reflects better self-rated health. Psychometric properties of the Swedish version of the SF-12 have been confirmed in three different samples., Cronbach's α for the MCS in the present study was 0.79.
The Minimal Insomnia Symptoms Scale
The well-validated Minimal Insomnia Symptoms Scale (MISS) was used to measure insomnia. The instrument includes the insomnia symptoms of difficulties initiating sleep, difficulties maintaining sleep and difficulties with nonrestorative sleep. Each item in the scale is based on a Likert scale ranging from no problems (0), small problems (1), moderate problems (2), great problems (3), to very great problems (4). Total score ranges from 0 and 12, wherein higher scores indicate more severe insomnia. A cutoff score of ≥6 is suggested to characterize adult subjects with clinical insomnia. Cronbach's α for MISS in the present study was 0.80.
Pearlin Mastery Scale
The well-established Pearlin Mastery Scale covers seven items that address the sense of control over what happens in life, as opposed to one's life being controlled by outside forces. Each item is scored on a Likert scale, ranging from 1 (strongly agree) to 4 (strongly disagree). Total score ranges from 7 to 28, and a higher scores indicate a stronger sense of mastery. Cronbach's α for Pearlin Mastery Scale in the present study was 0.74.
The Medical Outcomes Study-Social Support Survey
The Medical Outcomes Study-Social Support Survey (MOS-SSS) is an established and well-validated 19-item multidimensional questionnaire. MOS-SSS includes four subscales covering access to informational and emotional support, positive social interaction, affection, and tangible support. The items in each subscale measure how often certain types of support are available. The availability is rated on a 5-point scale from 1, none of the times, to 5, all of the time. Higher scores indicate a perception of greater access to support. In this study, the overall index score has been used. Cronbach's α for MOS-SSS in the present study was 0.95.
Descriptive statistics were used to assess patient's characteristics, as well as means, standard deviations, and zero-order correlations between measurement variables.
Structural equation modeling (SEM) was used to examine whether the theoretical model [Figure 1] was supported by the data. In the present study, as argued in the introduction social support was investigated as a first-order factor, and mental health was investigated as a secondary-order factor. Mastery, anxiety, depressive symptoms, and insomnia were in the model all thought to be associated to social support. Normal distribution of the study variables was assessed by Q-Q graphics and scatter matrix. There were no missing values in the data. Maximum likelihood was used to estimate parameters and examine the relationships between latent variables. The model fit was assessed by several indices including the χ2 of exact fit, comparative fit index (CFI), root mean square error of approximation (RMSEA), Tucker-Lewis index (TLI), and standardized root mean squared residual (SRMR). To determine whether model fit is good, a nonsignificant Chi-square test is needed. A nonsignificant Chi-square rejects the null hypothesis when there is no difference between the hypothesized model and the observed data. However, the Chi-square is sensitive to sample size. A Chi-square test will almost always be significant even with modest sample sizes. We, therefore, used other fit indices to assess model fit. Values of RMSEA <0.08, CFI, TLI >0.90 and SRMR <0.08 were used and are considered as acceptable. Age, gender, and aim of the treatment were included as covariates in the final model.
|Figure 1: Structural Equation Modelling. Note: Age, gender and aim of treatment were adjusted for score of Medical Outcomes Study-Social Support Survey and that of Mental Component Score. Higher score of Medical Outcomes Study-Social Support Survey and Mental Component Score indicate better social support and quality of life, respectively. *P < 0.1, **P < 0.001|
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Based on Anderson and Gerbing's (1988) recommendations, confirmatory factor analysis (CFA) was performed before running SEM to ensure that the latent variables are representatives of the observable variables (i.e., discriminative validity). Therefore, a full measurement model with six latent variables (i.e., mastery, anxiety, depressive symptoms, insomnia, social support, and mental health) and their corresponding observable variables was examined to assess measurement integrity. The data were analyzed using AMOS and SPSS software (IBM, New York, NY, USA) for Windows version 24.
The study protocol was approved by the Regional Ethics Review Board (reg. 549-10). Ethical considerations were made in accordance with the Swedish legislation (SFS 2003:460) and Helsinki Declaration, including voluntary participation, informed consent, and precautious protection of personal information while processing and presenting data.
| Results|| |
Patient and disease characteristics
Patient and disease characteristics are presented in [Table 1]. Mean age of participants was 55.9 (±16.7) years. Overall, most of the participants were living with a partner (76%), had a university education (47%) and were diagnosed with lymphoma (43%).
|Table 1: Patient and disease characteristics of patients affected by haematological diseases (n=120)|
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Means, standard deviations, and zero-order correlations between study variables are shown in [Table 2]. The results of the bivariate correlations between anxiety, depressive symptoms, insomnia, mastery, social support, and mental health demonstrated small-to-moderate significant correlations (P < 0.05). Mastery correlated significantly with anxiety (0.52; P < 0.01), depressive symptoms (0.58; P < 0.01), and social support (0.23; P < 0.05), but not with insomnia (0.18).
|Table 2: Means, standard deviations, and zero-order correlations between study variables (n=120)|
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The results of the CFA [Table 3] indicated that a six-factor model was found to have an acceptable fit (χ2 = 2417.95, degree of freedom = 1065, P = 0.001, TLI = 0.91, CFI = 0.93, and RMSEA = 0.080). The correlation between the latent variables ranged from − 0.68 to 0.78, with strong correlations between depressive symptoms and anxiety, anxiety and social support, and mastery and depressive symptoms. All the estimated parameters were statistically significant (P < 0.05). All standardized factor loadings were found be to higher than 0.50. The scales' internal consistency was acceptable (Cronbach α ranged from 0.74 to 0.95). Moreover, composite reliability was found to be above 0.70 across all six constructs. In addition, the measure of average variance extracted was above 0.50 for all six constructs [Table 3].
|Table 3: The results of the confirmatory factor analysis indicating an acceptable fit (n=120)|
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Structural equation modeling model
An SEM model was performed to examine the relationships between the variables in the proposed model. The hypothesized structural model is presented in [Figure 1]. The result revealed that the proposed model provided acceptable model fit (χ2 = 28.6, degree of freedom = 14, P = 0.001, TLI = 0.93, CFI = 0.97, and RMSEA = 0.074). All the estimated parameters, with the exception of mastery, were statistically significant (P < 0.05). Anxiety (−0.13), depressive symptoms (−0.44), and insomnia (−0.18) were negatively associated with perceived social support among the patients (P < 0.05). Therefore, the main effects of depressive symptoms and insomnia were confirmed on perceived social support. The moderator effect (depends on insomnia and depressive symptoms level) was then tested regarding the relationship between depressive symptoms and perceived social support. The analysis demonstrated that insomnia had a moderating effect. [Figure 2] shows that patients with a high level of depressive symptoms and high level of insomnia symptom scores experienced reduced access to social support. In addition, patients with low level of depressive symptoms and low level of insomnia symptoms scores experienced greater access to social support. For the interpretation of interaction effects, insomnia symptom scores should be considered point-to-point in the presence of depressive symptoms levels. Interestingly, as seen in [Figure 2], access to social support (i.e., the MOS-SSS score level) decreased more in case of a high level of depressive symptoms combined with a low level of insomnia. Furthermore, patients with a low level of depression symptoms and a high level of insomnia reported almost the same access to social support as those with a high level of depressive symptoms and low insomnia.
|Figure 2: Interaction effect between depression and in insomnia predicting perceived social support (i.e. Medical Outcomes Study-Social Support Survey). Low depression ≤7 on Hospital Anxiety and Depression Scale, high depression ≥8 on Hospital Anxiety and Depression Scale, low insomnia ≤5 on Minimal Insomnia Symptoms Scale, high insomnia ≥6 on Minimal Insomnia Symptoms Scale|
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| Discussion|| |
The main findings in this study revealed the combined effects of anxiety, depressive symptoms, and insomnia on social support and mental health. From a clinical nursing perspective, this implies the importance of exploring the associations between all these factors. We also found that insomnia acted as a moderating factor for social support. This could be of importance as it might be easier for nurses to explore insomnia compared to anxiety, depressive symptoms, and social support.
We found that the proportions of patients showing symptoms of anxiety and depression (i.e., percent in the categories at risk or having a probable disorder) are comparable to those found in other studies using HADS).,,, Prior research, show that middle age and female sex is associated with symptoms of anxiety. One clinical implication for nurses is therefore to be extra vigilant about these patients in clinical care since a considerable proportion are likely to experience elevated levels of psychological distress. On the other hand, the rather weak association between anxiety and social support in this study might be explained by the reasoning formulated by. They argue that perceived shortage of social support is associated with psychological distress only when there is a mismatch between desired and perceived support. Unrestricted access to support can be seen as beneficial, and may possibly assist those with a desire for support, regardless of the assessed support needs on the basis of existing individual's resources. We found that additional stressors, such as anxiety, depressive symptoms, and insomnia, were associated to perceived social support. Not surprisingly, we found that insomnia had a moderating effect on the relationship between depressive symptoms and social support [Figure 2]. A moderator is a third variable (i.e., in this case, insomnia) that affects the strength of the relationship between a dependent and independent variable (i.e., in this case, the correlation between depression and perceived support). As shown in [Figure 2], patients with low level of depressive symptoms and low level of insomnia perceived a high level of support (i.e., MOS-SSS). On the other hand, a patient with high depression scores, but low insomnia perceived low support. If both a high level of depressive symptoms and high level of insomnia symptoms occurred the lowest level of social support (i.e., MOS-SSS) was seen. A recent study has indicated that patients with higher social support are associated with better sleep quality in both insomnia and noninsomnia individuals. They stressed that higher levels of social support were most associated with shorter sleep latencies in those with insomnia. An awareness of patient-related characteristics, such as age and sex is valuable, but an additional understanding of how insomnia together depressive symptoms and anxiety can affect social support and mental health, as well as vice versa, can be decisive. Using extensive screening tools for all these concepts at the same time can be difficult in clinical practice. The use of short validated screening tools, such as HADS for depressive symptoms and anxiety, MISS for insomnia and MOS-SSS for social support is therefore recommended. Appropriate aftercare, to which patients can be referred to must also exist. To fully incorporate screening, referral, and treatment of distress, an organized care model is needed. However, such a comprehensive approach requires a multi-professional commitment. Future randomized controlled trials can focus on developing and evaluating such approaches. Another important aspect to consider when assessing the patient situation is that a patient's level of inner psychological resources (e.g., mastery, coping, control, self-efficacy, and optimism) might be difficult to interpret in a clinical situation. The difficulties can from a theoretical perspective be related to different taxonomies and inherent meanings of the concepts, or suspicions that the variables might be mediated by other variables. Exposure to HDs is a valid and often sudden threat to the future of life. For patients with HDs, this threat cannot really be controlled or altered by the patients themselves, contrasting for patients who suffer from, for example, lifestyle diseases, where the future to some extent is still within the control of the individual. stress that many patients are flexible in their coping, which implicates the need to be careful in the choice of screening tools, choice of interventions, as well as in follow-up assessments in clinical situations.
In a clinical situation, an alternative to measuring complex concepts, such as mastery and support, could be to focus on more easily measurable variables, such as satisfaction with information per se and satisfaction with the accessibility of information, respectively satisfaction with emotional support per se and its accessibility. Even though these concepts do not address the question of whether provided support efforts improve the patient's ability to manage, they might give a more tangible and thus valuable result. Reasonably, it is not only the desire for support and internal resources that justify the use of a patient-driven intervention, such as a web-based counseling service (BLINDED REFERENCE). As use also depends on the mechanism of action emphasize “engagement” as important for the effective use of digital behavior change interventions. This might be worth considering in both clinical practices, as well as in systematic evaluations in research studies. Similarly, it may be useful to consider the patient's communication skills, as a web-based counseling service requires that the patient masters a face-less conversation to become caring (40).
In addition to awareness of patients' different preferences and the varied ability and talent for using web-based support, one can consider whether a specific behavior (e.g., to request support) really is a result of the individual's reflective thoughts, or if it is an enacted habit due to a pervasive organizational culture., Demand that supportive interventions should be structured, described and evaluated according to the theoretical basis for the change inherent in the specific support intervention. In addition, calls for research testing the mechanisms of psychosocial interventions to theoretically better understand and thereby develop the ways interventions work and how to assess their effectiveness. However, choosing one approach to understand the mechanisms often means placing weight on some aspects (e.g., certain causal factors) at the expense of others, thus offering only partial understanding. In summary, there are associations between anxiety, depressive symptoms, mastery, social support, and mental health. Future research might focus on specific goal achievements with regard to a web-based counseling service's effects, for example, effective patient communication, as well as to understand the causal chain behind distress, mastery, social support, and other patient-reported outcome measures.
Strengths and limitations
This study is restricted due to its small sample size and recruitment of participants from only one University hospital. No a priori sample size calculation was conducted, but the proposed model [Figure 1] provided acceptable model fit, which we see as an indicator that the sample size was accurate to do the statistical analyses we have done (i.e., SEM). To assess, it is fully representative for the underlying population a larger strictly random sample would have been preferable. In addition, the sample is heterogeneous in terms of disease characteristics and the differences among diagnoses and medical variables are likely to be important for psychological outcomes. The results, therefore, need to be interpreted cautiously; nonetheless, an authentic clinical context at a Swedish university clinic forms its basis. To the set of variables chosen, there are likely additional factors which contribute to anxiety, depressive symptoms, and insomnia. Disease progression was, for example, not measured in the current study due to the heterogeneous sample, but most likely plays a role in the patients' distress levels which in turn might affect sleep. To evaluate causal relations a longitudinal design with a larger sample size and repeated measurement points would have been preferable.
A clinical implication is to, in an early stage, combine assessment tools for depressive symptoms, insomnia, and social support with verbal in-depth communication to explore each patient's inner conditions and adjust the psychosocial treatment in line with that. The nurse could after assessing the questionnaire scores focus on informational needs, need for emotional relief and/or encouragement in an individualized intervention. This can provide the basis for recommendations on interventions, but also how to conduct future research (i.e., both regarding explorative and interventional studies).
| Conclusion|| |
Associations exist among anxiety, depressive symptoms, insomnia, social support, and mental health. Thus, it is important that nurses, in addition to noting anxiety, depressive symptoms, and insomnia also are aware of the individual patient's resources in terms of existing social support and the patient's own capacity to master being ill, at the time being. This could routinely be assessed by in-depth communication or short validated screening tools, to identify those in need of supporting interventions.
We would like to thank Ingela Forsberg Larsson for assistance in the field.
The studied activity was funded by the Swedish Cancer Foundation. However, there is no conflict of interest between the authors and the organization under study, or the organization that financed the research. The authors have full control of all primary data and allow the journal to review their data if requested.
Financial support and sponsorship
The studied activity was funded by the Swedish Cancer Foundation. However, there is no conflict of interes between the authors and the organization under study, or the organization that financed the research. The authors have full control of all primary data and allow the journal to review their data if requested.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Schmaier AH, Lazarus HM. Concise Guide to Hematology. Hoboken, NJ, USA: Wiley-Blackwell; 2011.
Priscilla D, Hamidin A, Azhar MZ, Noorjan KO, Salmiah MS, Bahariah K, et al.
Assessment of depression and anxiety in haematological cancer patients and their relationship with quality of life. East Asian Arch Psychiatry 2011;21:108-14.
Mitchell AJ, Chan M, Bhatti H, Halton M, Grassi L, Johansen C, et al.
Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: A meta-analysis of 94 interview-based studies. Lancet Oncol 2011;12:160-74.
Clinton-McHarg T, Carey M, Sanson-Fisher R, Tzelepis F, Bryant J, Williamson A, et al.
Anxiety and depression among haematological cancer patients attending treatment centres: Prevalence and predictors. J Affect Disord 2014;165:176-81.
Johnsen AT, Tholstrup D, Petersen MA, Pedersen L, Groenvold M. Health related quality of life in a nationally representative sample of haematological patients. Eur J Haematol 2009;83:139-48.
Manitta V, Zordan R, Cole-Sinclair M, Nandurkar H, Philip J. The symptom burden of patients with hematological malignancy: A cross-sectional observational study. J Pain Symptom Manage 2011;42:432-42.
Harvey AG. Insomnia: Symptom or diagnosis? Clin Psychol Rev 2001;21:1037-59.
Lazarus R. Stress and Emotion: A New Synthesis. New York, USA: Springer Publishing Company; 2006.
Pearlin LI, Schooler C. The structure of coping. J Health Soc Behav 1978;19:2-21.
Cohen S, Gordon LU, Gottlieb BH, Fetzer I. Social Support Measurement and Intervention: A Guide for Health and Social Scientists. Oxford: Oxford University Press Oxford; 2000.
Adler NE, Page AE. Cancer Care for the Whole Patient: Meeting Psychosocial Health Needs. Washington (DC): National Academies Press (US), 2008.
Montgomery C, Pocock M, Titley K, Lloyd K. Predicting psychological distress in patients with leukaemia and lymphoma. J Psychosom Res 2003;54:289-92.
Wells KJ, Booth-Jones M, Jacobsen PB. Do coping and social support predict depression and anxiety in patients undergoing hematopoietic stem cell transplantation? J Psychosoc Oncol 2009;27:297-315.
Molassiotis A, Wilson B, Blair S, Howe T, Cavet J. Unmet supportive care needs, psychological well-being and quality of life in patients living with multiple myeloma and their partners. Psychooncology 2011;20:88-97.
Lobb EA, Joske D, Butow P, Kristjanson LJ, Cannell P, Cull G, et al.
When the safety net of treatment has been removed: Patients' unmet needs at the completion of treatment for haematological malignancies. Patient Educ Couns 2009;77:103-8.
Swash B, Hulbert-Williams N, Bramwell R. Unmet psychosocial needs in haematological cancer: A systematic review. Support Care Cancer 2014;22:1131-41.
Ancoli-Israel S. Sleep disturbances in cancer: A review. Sleep Med Res 2015;6:45-9.
Snaith RP, Zigmond AS. The hospital anxiety and depression scale. Br Med J (Clin Res Ed) 1986;292:344.
Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the hospital anxiety and depression scale. An updated literature review. J Psychosom Res 2002;52:69-77.
Ware J Jr., Kosinski M, Keller SD. A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Med Care 1996;34:220-33.
Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med 1991;32:705-14.
Jakobsson U, Westergren A, Lindskov S, Hagell P. Construct validity of the SF-12 in three different samples. J Eval Clin Pract 2012;18:560-6.
Broman JE, Smedje H, Mallon L, Hetta J. The minimal insomnia symptom scale (MISS): A brief measure of sleeping difficulties. Ups J Med Sci 2008;113:131-42.
Kline R. Principles and Practice of Structural Equation Modeling. 3rd
ed. New York: Guilford Press, USA; 2011.
Browne MW, Cudeck R. Alternative ways of assessing model fit. Sociol Methods Res 1992;21:230-58.
Iacobucci D. Structural equations modeling: Fit indices, sample size, and advanced topics. J Consum Psychol 2010;20:90-8.
World Medical Association. World medical association declaration of helsinki: Ethical principles for medical research involving human subjects. JAMA 2013;310:2191-4.
Santos FR, Kozasa EH, Chauffaille Mde L, Colleoni GW, Leite JR. Psychosocial adaptation and quality of life among Brazilian patients with different hematological malignancies. J Psychosom Res 2006;60:505-11.
Walsh D, Donnelly S, Rybicki L. The symptoms of advanced cancer: Relationship to age, gender, and performance status in 1,000 patients. Support Care Cancer 2000;8:175-9.
Linden W, Vodermaier A. Mismatch of desired versus perceived social support and associated levels of anxiety and depression in newly diagnosed cancer patients. Support Care Cancer 2012;20:1449-56.
Mickey RM, Dunn OJ, Clark V. Applied Statistics: Analysis of Variance and Regression. 3rd
ed. Hoboken, NJ: Wiley-Interscience; 2004.
Troxel WM, Buysse DJ, Monk TH, Begley A, Hall M. Does social support differentially affect sleep in older adults with versus without insomnia? J Psychosom Res 2010;69:459-66.
Mitchell AJ. Screening for cancer-related distress: When is implementation successful and when is it unsuccessful? Acta Oncol 2013;52:216-24.
Shaw JM, Price MA, Clayton JM, Grimison P, Shaw T, Rankin N, et al.
Developing a clinical pathway for the identification and management of anxiety and depression in adult cancer patients: An online Delphi consensus process. Support Care Cancer 2016;24:33-41.
Neipp M, López-Roig S, Pastor M. Control beliefs in cancer: A literature review. Anu Psicol 2007;38:333-555.
Hochhausen N, Altmaier EM, McQuellon R, Davies SM, Papadopolous E, Carter S, et al.
Social support, optimism, and self-efficacy predict physical and emotional well-being after bone marrow transplantation. J Psychosoc Oncol 2007;25:87-101.
Cheng C, Lau HP, Chan MP. Coping flexibility and psychological adjustment to stressful life changes: A meta-analytic review. Psychol Bull 2014;140:1582-607.
Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: A systematic review using principles from critical interpretive synthesis. Transl Behav Med 2017;7:254-67.
Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci 2015;10:53.
Ventura F, Ohlén J, Koinberg I. An integrative review of supportive e-health programs in cancer care. Eur J Oncol Nurs 2013;17:498-507.
Moyer A, Goldenberg M, Hall MA, Knapp-Oliver SK, Sohl SJ, Sarma EA, et al.
Mediators of change in psychosocial interventions for cancer patients: A systematic review. Behav Med 2012;38:90-114.
Kim KH. The relation among fit indexes, power, and sample size in structural equation modeling. Struct Equ Modeling 2005;12:368-90.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]