Introduction

Illness uncertainty is defined as “the inability of a person to determine the meaning of illness-related events” [1]. It can persist across the cancer trajectory from the time of diagnosis, through treatment, to long-term survivorship [2] and can be exacerbated by disease progression [3]. Illness uncertainty is widely recognized as a common and significant source of psychosocial stress among cancer patients [4], and studies have shown that increased uncertainty adversely affects cancer patients’ psychological adjustment [5, 6], health behaviors [7], and quality of life [8, 9]. This uncertainty can also extend to cancer patients’ family caregivers. In fact, patients’ partners have often reported higher levels of uncertainty compared to patients [3]. Research has also shown that increases in family caregivers’ illness uncertainty are associated with poorer psychological adjustment to the diagnoses and progression of cancer in patients [10, 11]. For example, uncertainty about the unknown outcomes of childhood cancers (e.g., late effects of cancer treatment, relapse) can increase parents’ distress and dysfunctional behaviors [10].

To address the negative impacts of illness uncertainty on the health outcomes (e.g., quality of life) [12], researchers and practitioners have developed and implemented various interventions to help cancer patients and their family caregivers manage illness uncertainty. Three previous literature reviews have synthesized research developments related to uncertainty management interventions. In their review of interventions for managing uncertainty and fear of recurrence in female breast cancer survivors [13], Dawson and colleagues reported that the main intervention components included mindfulness, more effective patient–provider communication, and stress management through counseling [13]. In their integrative literature review of uncertainty among children with chronic illnesses and their families, Gunter and Duke concluded that the education and psychosocial support is important in reducing uncertainty [14]. In their recent meta-analysis of psychosocial uncertainty management interventions among adult patients with various diagnoses (e.g., cancer, HIV, heart disease) and their family caregivers [15], Zhang et al. reported that psychosocial interventions are effective in reducing short- and long-term uncertainty both among patients and their family caregivers [15].

The existing reviews have focused on patients with various types of chronic illnesses who may face different challenges from patients with cancer [16] or patients with a gender-specific type of cancer (e.g., breast cancer). It therefore remains unclear whether the findings of these reviews are generalizable to patients with other types of cancer. Additionally, although research has shown that children and adolescents with cancer are affected by illness uncertainty [17, 18], no systematic review has examined their experiences of uncertainty management interventions. Researchers and practitioners stand to benefit from a comprehensive review of the literature about illness uncertainty interventions for patients with different types of cancer across age groups and their family caregivers. To this end, our study (a) systematically reviews and synthesizes results of uncertainty management interventions for cancer patients and their family caregivers, (b) identifies the strengths and gaps in this line of research, and (c) suggests directions for future research. Specifically, our systematic review examines the characteristics of participants in studies of illness uncertainty management interventions as well as the characteristics and outcomes of those interventions.

Methods

We adapted a comprehensive systematic review protocol based on the Cochrane Collaboration and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [19]. This protocol was registered with PROSPERO, an international prospective register of systematic reviews, prior to the beginning of the study (registration number CRD42019128004).

Eligibility criteria

We used the population, interventions, comparator, outcomes, and study (PICOS) design(s) to guide our inclusion criteria [20]. Studies eligible for inclusion are as follows: (a) targeted cancer patients and/or their family caregivers; (b) included uncertainty management in their research aims and/or as a part of the intervention’s contents; (c) reported intervention effects on illness uncertainty; (d) used randomized controlled trials (RCTs) or quasi-experimental designs; and (e) were published in English between January 1, 2000 and December 31, 2019. The search was not limited to studies using a control or comparison group.

Search methods

A university health sciences librarian helped to develop the search terms and identify relevant search databases. We conducted electronic literature searches using six databases: PubMed, Scopus, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, and Cochrane Database of Systematic Reviews. The database searches used Boolean terms “OR” and “AND” with combinations of the following search terms: (uncertainty) AND (cancer OR neoplasm*OR tumor OR myeloma OR oncolog*) AND (intervention OR program OR effect OR effectiveness OR treatment OR therapy) AND (patient OR patients OR caregiv* OR family OR families) AND (psych* OR mental* OR emotion*).

To identify studies potentially overlooked by our electronic searches, our research team conducted forward and backward citation chaining and hand searched Web of Science, Google Scholar, and prominent journals in the field to identify relevant articles for inclusion. Two coauthors independently reviewed the titles and abstracts and then—if an article merited further consideration—its full text using Covidence. Covidence is a web-based software platform designed to support the efficient production of systematic reviews [21]. We resolved any discrepancies in the two coauthors’ respective decisions regarding articles’ inclusion via group discussion among all team members.

Assessment of risk of bias in the included studies

We used the Cochrane Collaboration’s Risk of Bias Tool [22] to assess various sources of bias: selection bias, performance bias, detection bias, attrition bias, reporting bias, and other possible sources of bias (Appendix). Each domain was endorsed with a rating of “low risk,” “high risk,” or “unclear risk” following guideline’s criteria. Two coauthors independently conducted all risk of bias assessments, and we resolved any differences in their assessments through team discussion.

Data extraction and synthesis

Two of the coauthors independently extracted relevant data from the studies that met our inclusion criteria. We compared these extracted results and resolved any discrepancies through team discussion before merging the data. Because the included studies displayed different participant characteristics, intervention components, outcomes, and follow-up periods, we could not conduct a meta-analysis of their findings. We summarized the narratives and themes of each study and its results. Guided by House’s conceptualization of social support [23], we classified each intervention’s components into four categories: informational support, emotional support, appraisal support, and instrumental support.

Results

As shown in Fig. 1, our initial search of electronic databases and records and our hand searches of other sources yielded 1156 records. After removing duplicates, we identified 681 articles for title and abstract review, of which 49 were retained for a full-text review. After removing the studies that did not meet the inclusion criteria, we included 26 articles in this review.

Fig. 1
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PRISMA flow diagram

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Characteristics of studies

The majority of studies were conducted in the USA (n = 16). Others were conducted in Canada, China, Japan, United Kingdom (UK), and Vietnam. Eighteen studies were RCTs; one study used a RCT crossover design [24]. Eight studies were quasi-experimental studies (i.e., five “1-group pretest-posttest” studies, two “2-group pretest-posttest” studies, and one “2-group posttest” study) (Table 1). Among the 20 studies that included a control group, 12 studies used usual care, and eight included an active control group (e.g., a self-help group [25]; groups receiving recorded and written messages [26], telephone calls [27], and delayed interventions [28]). Sample sizes of included studies ranged from 9 [29] to 968 participants [30]. Among all studies, only seven studies were guided by theoretical frameworks such as the stress and coping theory (n = 5) [30,31,32,33,34], the uncertainty in illness theory (n = 1) [29], the double ABCX model (n = 1) [34], and the resilience model (n = 1) [35].

Table 1 Characteristics of study and participants and uncertainty outcomes
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Characteristics of participants

Twenty interventions were only offered to cancer patients. Two studies targeted family caregivers (i.e., the parents of children with cancer) [34, 36]. Four interventions were offered to both cancer patients and their partners and/or family caregivers [30,31,32,33].

Participants were recruited from hospitals, by invitation from a care provider, or via mailing and poster initiatives. The majority of studies recruited homogeneous patient populations, including patients with breast cancer (n = 8), prostate cancer (n = 6), brain tumor (n = 2), leukemia (n = 1), gynecological cancer (n = 1), and ovarian cancer (n = 1). Approximately 26.9% of studies (n = 7) targeted patients with various types of cancer. Regarding the stages of the cancer trajectory, these studies focused on cancer patients who were post-treatment (n = 8); patients with newly diagnosed cancer (n = 6); patients with advanced cancer (n = 5); patients undergoing active surveillance for cancer (n = 2); patients receiving treatment (n = 2); patients in recurrence (n = 2); and/or patients at a mix of stages (n = 1). With only two studies targeting parents of children and adolescents with cancer, the majority of the studies have focused on participants who were mostly female and White, and with an average age ranged from 44 to 72 years.

Intervention characteristics

Table 2 summarizes the interventions’ characteristics. Nineteen studies (73.1%) included uncertainty management as their main aim.

Table 2 Characteristics of the interventions
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Theoretical basis

Twelve studies (46%) described the theoretic frameworks used to guide different interventions. Five interventions were developed based on Mishel’s uncertainty in illness theory [27, 37,38,39,40]. Other theoretical models that guided the development of illness uncertainty management interventions also included the thematic counseling model [25], self-regulation theory [26], Leventhal’s common sense model [37], Brooten’s cost-quality model [41], self and family management framework [42], theory of self-efficacy, theory of stress and coping [31], cognitive behavioral therapy and acceptance and commitment therapy [43], and mindfulness-based stress reduction [44].

Contents and components

All of the interventions in our sampled studies included informational support that provided knowledge and resources related to illness, treatment, procedures, and symptom management. Eleven studies included emotional and psychological support from interventionist and peer groups. Nine studies included appraisal support that provided information and skills training for self-evaluation and positive perception, such as cognitive reframe and restructuring. Five studies included instrumental support that helped improve participants’ care coordination and ability to manage resources, referrals (social services, mental health, physical therapy), and continual follow-up schedules. Sixteen studies included two or more types of intervention components.

Mode of delivery, format, duration, and dosage

The studied interventions employed a variety of delivery modes. The majority of these interventions used both in-person and telephone delivery (n = 8) or in-person delivery (n = 7). The remaining eleven interventions used other delivery mechanisms including CD [27], DVDs [45, 46], telephone calls [38], informational booklets [42], internet [29], phone apps [36], or a combination of in-person delivery with DVD and CD content [43]. Most interventions were delivered to participants individually (n = 14), in a group format (n = 3) [36, 37, 44], or in family format (n = 4) [30, 32,33,34]. Other interventions used a combination of individual and group (n = 2) [25, 35], individual and family (n = 2) [38, 39], or group and family (n = 1) [31] delivery methods. The duration and dosage of the uncertainty management interventions varied across studies, ranging from one session [46] to 1-year period [35].

Interventionist

In thirteen studies, nurses delivered the interventions. Five interventions were delivered by professionals who had counseling or psychosocial background and training [28, 31, 35, 43, 44]. Five interventions were designed or delivered by multidisciplinary professionals [24, 34, 36, 37, 46]. One intervention was delivered by research staff [42] and one intervention design involved physicians [47]. One study did not report on the professional background of the interventionists [26].

Intervention outcome

Illness uncertainty assessment

The scale most commonly used to measure an intervention’s effect on uncertainty was the Mishel Uncertainty in Illness Scale (MUIS) (n = 19). This scale has different versions including MUIS-Community version [48], MUIS-Survivor version [27], MUIS-Short version [40], and Parents’ Perception of Uncertainty [36], which is based on the MUIS and measures parents’ uncertainty. Other studies measured uncertainty using the symptom and ambiguity subscale of MUIS [26, 49], the Decisional Conflict Scale-uncertainty subscale [46, 47], Parent Experience of Child Illness-Short Form [34], and the Intolerance of Uncertainty Scale [43, 44]. One study measured uncertainty using a 1-item scale [24]. One study measured uncertainty using three proxy measures (i.e., problem-solving, patient–provider communication, and cancer knowledge) [39]. Most studies assessed illness uncertainty outcomes using a longitudinal design with two time points (n = 6); three time points (n = 12); four time points (n = 6); or six time points (n = 1) [41].

Illness uncertainty outcomes

Overall, 65% of studies (n = 17) suggested that an illness uncertainty management intervention had a positive effect on uncertainty outcomes. Out of the eighteen RCTs, eleven studies demonstrated that the participants in the intervention group reported significant less uncertainty than those in the control group at follow-ups. Of these studies, eight studies assessed outcomes at multiple time points. Five studies reported more reduction in uncertainty in the intervention group over time [27, 28, 35, 39, 49]. Among eight quasi-experimental studies, three studies with a control group found that participants in the intervention groups reported significantly less uncertainty compared to those in the control group [36, 40, 45]. Among five quasi-experimental studies without a control group, three studies showed that intervention participants reported a significant decrease in uncertainty over time [34, 37, 46].

Risk of bias assessment

We evaluated each study’s risk of bias using the Cochrane Collaboration’s Risk of Bias Tool (Table 3). The eighteen RCTs had unclear (n = 11), high (n = 2), or low (n = 5) risk of bias. Most trials were classified as having unclear risk of bias because they did not describe the method used to generate the allocation sequence or report any strategies to maintain intervention fidelity (e.g., consistent intervention use among participants). We found that six quasi-experimental studies had high risk of bias. Most quasi-experimental studies used one-group pre- and post-designs without a control group; therefore, they had high risk of bias in random sequence generation and baseline imbalance.

Table 3 Assessment of study quality based on published data using Cochrane Collaboration’s criteria
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Discussion

This study systematically reviewed the characteristics and outcomes of illness uncertainty management–related interventions for adult and childhood cancer patients as well as their family caregivers. We found that all interventions evaluated in the included studies provided informational support. Other intervention components included emotional support, appraisal support, and instrumental support. The majority of studies used both in-person and telephone or in-person intervention delivery modes. The majority of studies suggested positive effects of illness uncertainty management–related interventions on uncertainty outcomes. With only two studies focused on parents of children and adolescents with cancer, the majority of interventions were only offered to cancer patients, who were mostly older adults, female, and White.

Overall, the majority of studies (65%) found that illness uncertainty management–related interventions had positive effects on uncertainty outcomes. Multi-component interventions, which used integrated resources to target multiple aspects of illness uncertainty such as informational support and emotional support, appear to be the most effective in managing illness uncertainty in cancer patients and their family caregivers. For example, Lebel et al. found that one intervention proved effective when employing a combination of introductory material about illness, cognitive restructuring and triggers, coping skills (e.g., relaxation, calming self-talk, guided imagery), and practice expressing emotion and confronting specific fears [37]. However, the positive effects of only a few interventions appeared to endure over time, possibly indicating that many interventions’ duration should be extended or include booster sessions as needed [38].

In general, we found that uncertainty management interventions were comprised of a variety of components including informational, emotional, appraisal, and instrumental support. Informational support is the key to helping cancer patients and their family caregivers manage uncertainty. Our findings corroborate those of two previous literature reviews of psychosocial interventions for managing uncertainty in childhood cancer patients and adult patients with different chronic illnesses [14, 15]. Findings from these reviews may also collectively indicate that individualized educational interventions provide information that empowers patients to successfully develop positive coping mechanisms. These findings are consistent with core tenets of Mishel’s uncertainty in illness theory, which posits that uncertainty occurs when patients lack the information or knowledge needed to fully interpret an illness and its treatment [1]. Informational support can enlarge patients’ information and knowledge base, enabling them to better understand an illness and thus experience less uncertainty. Moreover, when uncertainty occurs, it can be difficult for patients to form a cognitive structure [1]. Appraisal supports such as cognitive reframing can help patients reinterpret their illness and view a traumatic event as manageable [38]. Emotional and instrumental support can also provide patients with psychosocial resources to manage their uncertainty.

Most interventions were delivered using either in-person and telephone or in-person formats. This finding contrasts with Zhang et al.’s systematic review and meta-analysis of patients with chronic illnesses, which identified written educational materials as the most frequently used mode of intervention delivery [15]. Given the complexity of information provision and cancer patients’ potential for psychosocial distress, in-person meetings may be the preferred mode of intervention delivery. A format combining in-person and follow-up telephone components can both evaluate patients’ current understandings of their illness and help them reassess their emotional responses [27]. Our systematic review found limited evidence of the effectiveness of technology-based (e.g., web-based, apps) uncertainty management interventions [29, 36]. This area of research is still emerging, as indicated by the recent publication dates of studies of these technology-based interventions, their pilot and feasibility research aims, and their use of small sample sizes. Given these interventions’ potential ability to provide cost-effective psychosocial services [50] to manage uncertainty across the cancer care continuum, researchers should develop and evaluate technology-based interventions for uncertainty management using a rigorous research design (e.g., RCT) with a sufficiently powered sample.

Notably, only four interventions were offered jointly to cancer patients and their spouses or partners, and only one of these reported significant improvement in the uncertainty outcome among cancer patients and their spouses [33]. This comparatively low number perhaps reflects the challenges to conducting family-based research, such as explaining the purpose of the research to multiple participants, having an extended recruitment phase that involves contacting and obtaining consent from more people, and high refusal rates [31, 51]. The small number of interventions that included spouses or partners is striking, as family caregivers play key roles in supporting cancer patients [52] and often experience higher levels of uncertainty than patients [3]. Interventions delivered to patients’ family caregivers can improve their knowledge, coping skills, and quality of life [53], which will in turn improve cancer patients’ care and outcomes (e.g., quality of life) given the synergetic interdependent relationships between cancer patients and their family caregivers [54]. There is a pressing need for future research to inform the development of interventions designed to manage uncertainty for both cancer patients and their caregivers.

Our review also indicates that future research must diversify the age, gender, and racial distributions of sample populations used to evaluate the outcomes of illness uncertainty interventions. Although previous research has shown that uncertainty is a common experience for children and adolescents with cancer [14], we identified only two interventions that assisted the parents of children with cancer to manage uncertainty [34, 36], and no intervention in our sample targeted children and adolescents with cancer. Therefore, because experiences of uncertainty can vary across different age groups or developmental stages [55], researchers should develop age-appropriate interventions that take into account the specific needs of children and adolescents with cancer. Furthermore, most of the participants in the intervention studies in this review were female, White, and older adult cancer patients. Future research regarding illness uncertainty management interventions should create strategies to increase the number of male patients and family caregivers in intervention programming. Although recruiting men for clinical trials is difficult because men are often reluctant to access services and to recognize that they need help [31], male cancer patients (e.g., prostate cancer) often experience uncertainty about their treatment decision-making and/or their management of cancer treatment-related symptoms and side effects [3, 56]. Finally, although two interventions succeed to include a sufficient number of African American cancer patients [27, 38], the majority of the study populations were White. Given that one study found that African American cancer patients reported higher levels of uncertainty than White cancer patients [3], future intervention should be tailored to help patients from minority groups and researchers should gather data about the effects of interventions using more diverse samples of cancer patients and their family caregivers.

According to the Cochrane Collaboration’s Risk of Bias Tool, most studies had “unclear” or “high risk” of bias due to their unclear reporting. Many studies have unclear reporting of the study procedures that do not meet reporting standards. Future studies should provide complete, clear, and transparent information about how to create and present a research methodology and findings in accordance with CONSORT criteria and flowchart templates.

Limitations

This review’s findings should be considered in light of several limitations. The studies we sampled differed considerably in their study participants’ demographic variables (e.g., older, female, and White cancer patients), types of interventions, outcome measures, and timing of follow-ups. We could not conduct a meta-analysis that synthesizes their discrepant findings, which would have provided more rigorous evidence of the effects of uncertainty management interventions for cancer patients and their family caregivers. Additionally, our review only focused on interventions’ effects on uncertainty outcomes. Future research should examine the effects of uncertainty management interventions on other outcomes in order to get a more comprehensive picture of the effect of interventions. We also focused only on peer-reviewed published studies and may have missed relevant studies from the gray literature. Excluding unpublished studies likely increases the potential for biased findings; however, we included studies that reported non-significant results, thus mitigating the possibility of publication selection bias.

Clinical and research implications

Our review has numerous implications for future clinical practice and research. Providing uncertainty management interventions with multiple components at different phases of the cancer trajectory may significantly reduce uncertainty and facilitate adaptation among patients and family caregivers. There is strong evidentiary support that multi-component interventions yield effective outcomes. However, more research is needed to compare the discrete effects of different intervention components, modes of delivery, and formats on uncertainty management outcomes among cancer patients and their family caregivers. This research should also include study populations with diverse backgrounds (e.g., by age, gender, and/or race), and in particular seek to engage children and adolescents with cancer, males, and African Americans—all groups for whom few if any tailored uncertainty management interventions currently exist.

Conclusion

This systematic review underlines the promising potential of uncertainty management interventions—especially interventions involving multiple components including informational, emotional, appraisal, and instrumental support—to help cancer patients and their family caregivers manage illness uncertainty. Future research needs to employ a rigorous research methodology in order to test uncertainty management interventions among a diverse population and to ensure complete and accurate reporting of the research procedures and findings.