Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at, first published .
Telehealth-Based Music Therapy Versus Cognitive Behavioral Therapy for Anxiety in Cancer Survivors: Rationale and Protocol for a Comparative Effectiveness Trial

Telehealth-Based Music Therapy Versus Cognitive Behavioral Therapy for Anxiety in Cancer Survivors: Rationale and Protocol for a Comparative Effectiveness Trial

Telehealth-Based Music Therapy Versus Cognitive Behavioral Therapy for Anxiety in Cancer Survivors: Rationale and Protocol for a Comparative Effectiveness Trial


1Memorial Sloan Kettering Cancer Center, New York, NY, United States

2Miami Cancer Institute, Miami, FL, United States

3Society for Integrative Oncology, Washington, DC, United States

4Young Survival Coalition, New York, NY, United States

5Thomas Jefferson University, Philadelphia, PA, United States

6Drexel University, Philadelphia, PA, United States

Corresponding Author:

Kevin T Liou, MD

Memorial Sloan Kettering Cancer Center

321 East 61st Street

New York, NY, 10065

United States

Phone: 1 646 608 8558

Fax:1 212 717 3185


Background: Cancer survivors represent one of the fastest growing populations in the United States. Unfortunately, nearly 1 in 3 survivors experience anxiety symptoms as a long-term consequence of cancer and its treatment. Characterized by restlessness, muscle tension, and worry, anxiety worsens the quality of life; impairs daily functioning; and is associated with poor sleep, depressed mood, and fatigue. Although pharmacological treatment options are available, polypharmacy has become a growing concern for cancer survivors. Music therapy (MT) and cognitive behavioral therapy (CBT) are evidence-based, nonpharmacological treatments that have demonstrated effectiveness in treating anxiety symptoms in cancer populations and can be adapted for remote delivery to increase access to mental health treatments. However, the comparative effectiveness of these 2 interventions delivered via telehealth is unknown.

Objective: The aims of the Music Therapy Versus Cognitive Behavioral Therapy for Cancer-related Anxiety (MELODY) study are to determine the comparative effectiveness of telehealth-based MT versus telehealth-based CBT for anxiety and comorbid symptoms in cancer survivors and to identify patient-level factors associated with greater anxiety symptom reduction for MT and CBT.

Methods: The MELODY study is a 2-arm, parallel-group randomized clinical trial that aims to compare the effectiveness of MT versus CBT for anxiety and comorbid symptoms. The trial will enroll 300 English- or Spanish-speaking survivors of any cancer type or stage who have experienced anxiety symptoms for at least 1 month. Participants will receive 7 weekly sessions of MT or CBT delivered remotely via Zoom (Zoom Video Communications, Inc) over 7 weeks. Validated instruments to assess anxiety (primary outcome), comorbid symptoms (fatigue, depression, insomnia, pain, and cognitive dysfunction), and health-related quality of life will be administered at baseline and at weeks 4, 8 (end of treatment), 16, and 26. Semistructured interviews will be conducted at week 8 with a subsample of 60 participants (30 per treatment arm) to understand individual experiences with the treatment sessions and their impact.

Results: The first study participant was enrolled in February 2022. As of January 2023, 151 participants have been enrolled. The trial is expected to be completed by September 2024.

Conclusions: This study is the first and largest randomized clinical trial to compare the short- and long-term effectiveness of remotely delivered MT and CBT for anxiety in cancer survivors. Limitations include the lack of usual care or placebo control groups and the lack of formal diagnostic assessments for psychiatric disorders among trial participants. The study findings will help guide treatment decisions for 2 evidence-based, scalable, and accessible interventions to promote mental well-being during cancer survivorship.

International Registered Report Identifier (IRRID): DERR1-10.2196/46281

JMIR Res Protoc 2023;12:e46281



The Burden of Anxiety During Cancer Survivorship

With advances in oncology care, the number of cancer survivors in the United States is expected to increase dramatically and exceed 22 million by the end of this decade [1]. A meta-analysis identified anxiety as one of the most common mental health issues faced by cancer survivors [2]. Compared with the general population, cancer survivors experience higher rates of anxiety [2-5], and up to one-third of them experience clinically meaningful anxiety symptoms [3,5-9]. Characterized by restlessness, muscle tension, and worry that is difficult to control, anxiety is a highly disruptive symptom associated with poor sleep [10], depressed mood [11-13], and fatigue [14-17]. Anxiety also contributes to poor treatment adherence [18-20] and increased health care expenditures [21,22]. As a primary driver of poor quality of life among cancer survivors [23-26], anxiety is an important issue to address in this rapidly growing population.

Although pharmacological treatment options for anxiety are available [27], polypharmacy is a growing concern among cancer survivors [28-31]. Compared with the general population, cancer survivors have much higher rates of polypharmacy, with 64% taking ≥5 unique medications [30]. One-third of survivors receive at least 1 psychotropic medication (eg, anxiolytics and antidepressants), and nearly 1 in 6 survivors report using ≥2 classes of psychotropic medications [31]. Psychotropic polypharmacy is associated with poor quality of life, impaired physical and mental functioning, financial toxicity, and higher risk of side effects and drug interactions [31]. These challenges of polypharmacy highlight the need for nonpharmacological treatment options for anxiety during cancer survivorship.

Cognitive Behavioral Therapy: An Effective First-Line Treatment for Anxiety

Cognitive behavioral therapy (CBT) is an evidence-based, nonpharmacological intervention delivered by licensed mental health professionals [32,33]. The American Society of Clinical Oncology (ASCO) [27] and the National Comprehensive Cancer Network (NCCN) [34] recommend CBT for treatment of anxiety in patients with cancer. Informed by the cognitive behavioral model of anxiety, CBT focuses on the relationship between thoughts, behaviors, and emotions and how thoughts and behaviors can exacerbate or reduce anxiety [35-37]. The therapeutic components of CBT consist of psychoeducation on anxiety, relaxation techniques, cognitive restructuring, and strategies for planning activity engagement and managing realistic worries [38,39]. Meta-analyses demonstrated moderate to large effects of CBT on anxiety symptoms in patients with cancer and cancer survivors relative to control conditions, with effect sizes ranging from 0.42 to 1.10 [40-43]. CBT is widely considered an effective first-line treatment for anxiety in people affected by cancer [27,34].

Despite the established effectiveness of CBT, studies have demonstrated that 20% to 25% of CBT participants fail to complete a full treatment course [44,45] and 37% do not achieve clinically meaningful improvements in anxiety symptoms [46,47]. Furthermore, social stigma surrounding psychotherapy exists in some demographic groups and cultures, and research shows that underserved racial or ethnic groups are more likely than White patients to delay, avoid, or drop out of mental health treatments [48,49]. These challenges of CBT highlight the importance of studying other nonpharmacological treatments that are not only effective but also culturally acceptable to diverse cancer survivors.

Music Therapy: An Evidence-Based Anxiety Treatment With Increasing Availability at Cancer Centers

Music therapy (MT) is a nonpharmacological, evidence-based intervention in which board-certified music therapists engage patients in personally tailored experiences with music to achieve therapeutic goals [50]. These experiences range from music-guided relaxation to more active forms of musical engagement, including singing and improvising music [50]. The social-cognitive processing model of emotional adjustment in cancer is one of several possible models that informs the use of MT for anxiety management in cancer survivors [51]. Supported by empirical studies [52,53], this model conceptualizes the cancer treatment journey as a disruptive, trauma-like experience that must be cognitively processed in a supportive social context for healthy emotional adaptation to occur [51,52]. A growing body of research demonstrates the capacity of MT to influence these social-cognitive processes central to anxiety. Interactive music experiences have been shown to build social connections and promote a sense of belonging [54-56]. Prior studies also suggest that MT provides novel, creative outlets (eg, through songwriting) to cognitively process past traumatic experiences that may otherwise be difficult to verbalize [57,58]. In addition to its social-cognitive effects, musical engagement has been shown to modulate brain regions (ie, amygdala) [54] and biological systems (ie, hypothalamic-pituitary-adrenal axis [59-61] and autonomic nervous system [62-65]) responsible for emotional regulation and implicated in anxiety and mood disorders [66-69]. Importantly, ethnographic and phylogenetic research has identified music as a defining characteristic of humankind across many cultures worldwide [70,71]. The multicultural presence of music supports the potential of MT to resonate with a diverse population of cancer survivors.

MT has a growing evidence base for cancer symptom management [72-75] and is recommended by ASCO [76], NCCN [34], and the Society for Integrative Oncology [77,78] as a treatment option for anxiety in cancer populations. A recent Cochrane review (81 trials, N=5576) found that MT was associated with a large reduction in anxiety symptoms compared with usual care [79]; MT is also offered at approximately 75% of the National Cancer Institute (NCI)–designated Comprehensive Cancer Centers and 55% of community-based cancer programs [80]. Owing to its growing evidence base and availability, MT represents a promising alternative to CBT for the treatment of anxiety.

Digital Transformation of Health Care During COVID-19: A Timely Opportunity to Improve Access to Mental Health Resources

In the past 2 decades, the percentage of US adults who use the internet has increased from 52% to 90% [81]. Approximately 59% to 79% have access to home broadband internet services [81] and 37% use smartphones as their primary access to the internet [82]. Cancer centers across the country are increasingly leveraging digital technology to monitor and manage symptoms remotely [83]. The COVID-19 pandemic accelerated these digital trends, and the use of videoconferencing for telehealth services increased by 8700% at the peak of the pandemic [84].

Although key disparities in digital access and literacy remain [85], the widespread adoption of videoconferencing offers a promising platform for reducing barriers to mental health services. Therapists have successfully used videoconferencing to deliver MT remotely to military populations [86-90] and patients with autism [91]. Therapists have also used videoconferencing to deliver MT services to inpatient [92,93] and oncology settings [94]. At the outset of the pandemic, the American Music Therapy Association and other MT professionals rapidly developed and implemented telehealth guidelines and resources to facilitate the telehealth-based delivery of MT services to patients isolated at home [92,95]. Many music therapists have shifted their clinical practices to web-based platforms [96,97]. There is also an extensive body of research (>100 trials) on internet-delivered CBT interventions [98,99], and studies have consistently demonstrated that CBT delivered remotely is as effective and acceptable as in-person treatments for anxiety [100]. Thus, both MT and CBT are equipped for the digital health care landscape, with unique potential for scalability to reduce barriers to mental health services.

Gaps in the Evidence: Treatment Dilemmas Facing Patients and Clinicians in the New Digital Health Care Landscape

Although CBT is widely considered a first-line treatment for anxiety, not all individuals are able to complete a full treatment course [44,45] or achieve meaningful improvements in their symptoms [46,47]. People may also be reluctant to pursue CBT because of the sociocultural stigma surrounding psychotherapy in different groups and communities [48,49]. For individuals who do not respond or do not wish to pursue CBT, evidence is lacking on whether MT is an effective, noninferior treatment alternative to CBT.

To inform treatment decision-making, the American Psychological Association, in its resolution on the Recognition of Psychotherapy Effectiveness, has called for “continued and further research on the comparative effectiveness” of CBT and other psychotherapeutic interventions [101]. Furthermore, as health care continues to undergo rapid digital transformation, there is a critical need for additional research into telehealth interventions. Although CBT research has compared remote delivery versus traditional in-person formats, few studies have compared telehealth-based CBT with other telehealth interventions, such as MT [98,100]. In their systematic review of digital health interventions for cancer survivors, Harris et al [83] emphasized that “one size may not fit all” and there is a “need to identify who is most likely to benefit from digital interventions.” Comparative effectiveness research on different telehealth-based treatments (ie, CBT vs MT) is essential to guide timely and patient-centered decision-making and help patients and clinicians navigate this new digital health care landscape.

The Music Therapy Versus Cognitive Behavioral Therapy for Cancer-Related Anxiety Trial: Study Aims and Hypotheses

The Music Therapy Versus Cognitive Behavioral Therapy for Cancer-related Anxiety (MELODY) trial seeks to address the following aims:

The primary aim is to determine the comparative effectiveness of telehealth-based MT versus telehealth-based CBT for anxiety and comorbid symptoms in cancer survivors.

  • Hypothesis 1(a): MT will be noninferior to CBT in treating anxiety symptoms among survivors at week 8 (end of treatment) and week 26 (long-term follow-up).
  • Hypothesis 1(b): Compared with CBT, MT will be associated with significantly greater improvement in fatigue co-occurring with anxiety.
  • Exploratory hypothesis: Survivors may have unique experiences with MT and CBT for anxiety during cancer survivorship.

The secondary aim is to identify patient-level factors associated with greater anxiety symptom reduction after MT and CBT.

  • Hypothesis 2: Specific sociodemographic characteristics (eg, age, sex, race, and education) or psychological attributes (ie, expectancy) will be associated with treatment response to MT or CBT.

Study Design

The MELODY study is a 2-arm, parallel-group, pragmatic randomized clinical trial that aims to compare the effectiveness of MT versus CBT for anxiety and comorbid symptoms in a diverse and heterogeneous sample of 300 cancer survivors (Figure 1). This study will be conducted at the Memorial Sloan Kettering Cancer Center (MSK), an NCI-designated Comprehensive Cancer Center, as well as the Miami Cancer Institute (MCI) and Drexel University. These sites were chosen in part for their potential to help recruit a diverse study population. Interventions will be delivered weekly via Zoom (Zoom Video Communications, Inc) over 7 weeks. Study assessments will be administered at baseline and at weeks 4, 8, 16, and 26. This study will be conducted in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines for nonpharmacological interventions [102], the reporting guidelines for music-based interventions [103], and the National Institutes of Health Protocol Template for Behavioral and Social Sciences Research [104]. This study has also been registered at (NCT05215353).

Figure 1. Study schema.

Ethics Approval

The study was approved by the institutional review board (IRB) at MSK on December 22, 2021 (IRB: 21-516). MSK serves as the IRB of record for both MCI and Drexel University.

Eligibility Criteria

The eligibility criteria were designed to be broad and consistent with a pragmatic design while ensuring participant safety and scientific rigor. Participants are eligible if they are English- or Spanish-speaking, are aged ≥18 years, have a prior cancer diagnosis of any type or stage, are free of oncological disease, have a score of ≥8 on the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS), report anxiety symptoms lasting at least 1 month, and have access to Zoom and a quiet or private location. Participants will be excluded if they had completed active cancer treatment (eg, surgery, radiation, and chemotherapy) less than 1 month before enrollment; however, participants receiving maintenance hormonal or targeted therapies will be allowed to enroll. Participants will also be excluded if they have active suicidal ideation, bipolar disorder, schizophrenia, or substance abuse; have a score of ≥10 indicative of cognitive impairment on the Blessed Orientation-Memory-Concentration; or received a treatment course of ≥7 MT or CBT sessions for anxiety symptoms within the last 6 months.


This study will use a multimodal recruitment strategy that comprises population-based methods, clinician referrals, and community outreach and engagement, which are described as follows:

  1. Population-based methods: MSK and MCI patient databases will be queried to identify survivors who meet basic eligibility criteria. MSK catchment areas include urban and suburban locations in New York, Long Island, and New Jersey, whereas MCI catchment areas include urban and suburban locations in South Florida with access to a large Hispanic population. Drexel University investigators will query the Pennsylvania Cancer Registry to identify potential participants from rural populations. A recruitment letter will be sent to the potential participants identified on these databases and registries. The recruitment letter will introduce the study and include instructions for interested persons to contact the study team.
  2. Clinician referrals: potential participants will also be identified and referred by clinicians at MSK, MCI, or other health care settings. To facilitate referrals, the study team will educate clinicians about the study, focusing on those who care for cancer survivors.
  3. Community outreach and engagement: information about the study will also be posted on social media outlets,, and public-facing websites. The study team will also present the study at scientific conferences, cancer survivorship support groups, community organizations, and other patient advocacy groups.

The goal of this multimodal approach is to enroll a diverse sample that includes traditionally underrepresented sociodemographic groups. The recruitment strategies were developed jointly by the study team and the patient and stakeholder advisory board, which includes patient advocates, cancer survivors, community leaders, and clinical stakeholders. Diverse study representation will facilitate careful exploration of the differences between participant subgroups to inform personalized decision-making.

Once interested and potentially eligible participants are identified, the research staff will schedule them for an initial screening visit. If participants meet the basic eligibility criteria, they will then be scheduled for a telehealth visit with a study clinician to confirm their eligibility. If a participant is deemed eligible, the research staff will explain the study procedures and request informed consent. Once consent is obtained, the participants will complete the baseline assessments.


After completing the baseline assessments, participants will be randomized using MSK’s secure computer system that ensures full allocation concealment. Randomization will be 1:1 (MT:CBT) using randomly permuted blocks of random length, which are stratified by study site, current anxiety medication use (yes or no), and preferred language (English or Spanish). The principal investigator and biostatistician will be blinded to treatment assignments.


Treatment Course

Study participants will receive 7 weekly 60-minute MT or CBT sessions over the first 7 weeks. All sessions will be delivered remotely through Zoom’s Health Insurance Portability and Accountability Act (HIPAA)–compliant, encrypted, and passcode-protected videoconferencing software. Although the duration of these types of treatments may vary in real-world settings, this study will match the MT and CBT groups for patient-therapist contact time to ensure rigorous comparative effectiveness comparison.

MT Protocol

MT will be delivered by board-certified music therapists. The MT protocol is based on the neuroscience of music [54,105], the social-cognitive processing model [51,52], the scientific literature on MT [72], and the clinical and research experience of the study team [106]. Healthy psychological adjustment during cancer survivorship requires the processing of feelings and thoughts related to one’s cancer journey in a supportive context [51,52]. Therefore, the intervention protocol is structured to build patient-therapist rapport and allow for greater emotional expressivity and deeper cognitive processing. Guided by the social-cognitive processing model [51,52], the first few sessions will focus on building a trusting relationship with the therapist, whereas the later sessions will focus on the cognitive processing of cancer-related experiences and current fears, worries, and hopes, using playlist creation and songwriting as an outlet for reflection, expression, and meaning making. Research also suggests that patients prefer receptive modes of engagement during their initial exposure to MT [75,107]. As such, the early sessions will focus on receptive techniques (eg, music-guided stress management), and the later sessions will progress to more active MT techniques (eg, songwriting). The protocol includes homework activities in between sessions to promote self-management skills (eg, the use of music-guided deep breathing), strengthen social connections (eg, sharing meaningful songs with loved ones), provide outlets for cognitive processing (eg, writing or reflecting on themes for song lyrics), and serve as transitions to the subsequent sessions.

CBT Protocol

The CBT protocol is based on the scientific literature [32,33] and the clinical and research experience of the study team [108]. CBT therapists will include licensed social workers because they are the most commonly employed mental health providers in cancer care and, therefore, reflect the real-world practice and implementation of CBT in oncology settings [109]. Informed by the cognitive behavioral model of anxiety, CBT focuses on addressing the thoughts and behaviors that exacerbate or reduce anxiety [35-37]. The protocol consists of psychoeducation on anxiety, relaxation techniques, cognitive restructuring, and strategies for planning activity engagement and managing realistic worries [38,39]. These components target the somatic symptoms of anxiety (eg, muscle tension) as well as the thoughts (eg, “What if my next scan is bad?”) and behaviors (eg, excessive symptom monitoring) that trigger and exacerbate anxiety. Once identified, these problematic thoughts and behaviors are replaced by thoughts and behaviors that prevent and reduce anxiety. Each session will follow a consistent format that includes an overview of the session content, review of the homework exercise from the prior session, information on a new skill for managing anxiety, discussion of the upcoming homework exercise, and a plan for completing the exercise before the next session. Patients will receive a workbook with materials for each session and homework activities that promote practice of the skill learned during the session.

Interventionist Training and Treatment Fidelity

All interventionists will be trained about the specific research protocol and educated on the importance of adherence to the protocol methods. The interventionists will be either English speaking or bilingual in English and Spanish. The training of study interventionists will include didactic information on anxiety in cancer survivors and review of intervention materials. Interventionists will be provided with ongoing supervision over the course of the trial. During the trial, the CBT and MT sessions will be recorded and stored on secure, encrypted MSK servers. To ensure that study therapists adhere to the treatment protocol, document treatments appropriately, and maintain fidelity to the core functions of the intervention, the session recordings for the first 2 patients seen by a therapist will be reviewed. The review of session recordings will be based on treatment fidelity checklists outlining the core intervention components. If a therapist adheres to at least 80% of the treatment fidelity checklist items, then subsequent fidelity monitoring will be reduced to 2 randomly selected session recordings per patient. Deviations from the MT or CBT protocols will be discussed with study therapists during supervision meetings, and strategies will be suggested to minimize the number of deviations. Therapists who fail to adhere to at least 80% of the treatment fidelity checklist items will be retrained. Similar treatment fidelity strategies have been successful in other MT [106] and CBT trials [108,110] of patients with cancer conducted by the study team.

Study Assessment Procedures


Patient-reported outcomes (Table 1) will be completed on the web using Research Electronic Data Capture (REDCap; Vanderbilt University) [111]. Participants will also have the option to complete these study assessments on paper or over the telephone with research staff. Study assessment materials will be available in English or Spanish. Patient-reported outcome measures for anxiety, comorbid symptoms, and quality of life have been validated in Spanish [112-117]. Where needed, Spanish translation of other assessments took place in standard forward and reverse translation.

Table 1. Summary of patient-reported outcomes.
Primary or secondaryName of outcomeValidated instrumentTime points (weeks)
PrimaryAnxietyHADSa0, 4, 8, 16, and 26
SecondaryDepressionHADS0, 4, 8, 16, and 26
SecondaryFatigueBFIb0, 4, 8, 16, and 26
SecondaryInsomniaISIc0, 4, 8, 16, and 26
SecondaryPainBPId0, 4, 8, 16, and 26
SecondaryCognitive dysfunctionFACT-Coge0, 4, 8, 16, and 26
SecondaryQuality of lifePROMIS–Global Healthf0, 4, 8, 16, and 26
SecondaryTreatment expectancyMETEg0 and 8
SecondaryTreatment preferenceTreatment preference Scale0 and 8
SecondaryMusic rewardBMRQh0
SecondaryMental health stigmaSSRPHi0
SecondaryGeneralized anxiety disorderGAD-7j0

aHADS: Hospital Anxiety and Depression Scale.

bBFI: Brief Fatigue Inventory.

cISI: Insomnia Severity Index.

dBPI: Brief Pain Inventory.

eFACT-Cog: Functional Assessment of Cancer Therapy–Cognitive Function.

fPROMIS–Global Health: Patient-Reported Outcomes Measurement Information System–Global Health.

gMETE: Mao Expectancy of Treatment Effects.

hBMRQ: Barcelona Music Reward Questionnaire.

iSSRPH: Stigma Scale for Receiving Psychological Help.

jGAD-7: Generalized Anxiety Disorder 7-Item Scale.


The primary outcome is severity of anxiety symptoms, as assessed using the HADS anxiety subscale. The reliability, validity, and factor structure of this 7-item subscale has been established in patients with cancer with a Cronbach α of .83 [118,119]. A score of ≥8 indicates the presence of anxiety symptoms. Research has identified a minimal clinically important difference (MCID) of 1.7 points [120]. The HADS anxiety subscale will be administered at baseline and at weeks 4, 8, 16, and 26.

Comorbid Symptoms and Quality of Life

Given that anxiety is strongly associated with depressed mood [11-13], fatigue [14-17], insomnia [10], pain [121], and cognitive dysfunction [122], these comorbid symptoms will be assessed using validated instruments. Depressive symptoms will be assessed using the HADS depression subscale (Cronbach α=.79) [118,119]. Fatigue will be assessed using the Brief Fatigue Inventory (BFI), a 9-item scale that has been validated in cancer populations with a Cronbach α of .96 [123]. Insomnia symptoms will be assessed using the Insomnia Severity Index [124], a 7-item instrument validated in cancer populations with a Cronbach α of .90 [125]. Pain severity and pain-related interference will be assessed using the Brief Pain Inventory (BPI). The BPI has been demonstrated to be a reliable, valid, and responsive measure with a Cronbach α of .77 to .91 [126]. Cognitive difficulties will be assessed using the Functional Assessment of Cancer Therapy–Cognitive Function (FACT-Cog), version 3, a 37-item questionnaire with 4 subscales (perceived cognitive impairments, impact on quality of life, comments from others, and perceived cognitive abilities). The FACT-Cog is a reliable instrument validated in cancer populations with a Cronbach α of .89 [127]. The FACT-Cog instrument will only be administered to participants who reply “YES” to the following question at baseline: “Are you experiencing difficulties with memory, concentration, or other cognitive abilities?” As anxiety is a key determinant of quality of life in cancer survivors [23-26], the Patient-Reported Outcomes Measurement Information System–Global Health (PROMIS–Global Health) will also be administered. This validated measure contains 2 domains related to quality of life: mental health (Cronbach α=.86) and physical health (Cronbach α=.81) [128]. These measures will be administered at baseline and at weeks 4, 8, 16, and 26.

Treatment Expectancy

The Mao Expectancy of Treatment Effects (METE) is a 4-item instrument originally developed as the Acupuncture Expectancy Scale to measure outcome expectancy. It has demonstrated validity and reliability (Cronbach α=.82) and is correlated with patient self-reported efficacy and satisfaction [129-131]. Prior research has also demonstrated that outcome expectancy is associated with treatment response [129,132,133] and can, therefore, help inform personalized treatment decisions. The score ranges from 4 to 20, with higher scores indicating greater expectancy of benefit. This measure has been adapted for the MELODY study to assess patient expectancy of MT and CBT. The METE will be administered at baseline before receiving treatment. It will also be administered at week 8 to explore whether the expectancy changes after the treatment process.

Treatment Preference

As participants may have different preferences toward mental health treatment based on health beliefs or prior experiences with interventions, they will be asked at baseline whether they prefer MT or CBT or none [134]. Their preferences will also be assessed at week 8 to explore whether preferences change after their experiences with either MT or CBT.

Music Reward

Prior research has demonstrated variability in how people derive reward and pleasure from music, which may affect their experience with MT [135]. Therefore, the Barcelona Music Reward Questionnaire will be administered at baseline to assess the level of reward associated with music. This instrument has been validated with a Cronbach α of .92 [135].

Stigma of Receiving Psychological Help

In some groups and cultures, there is stigma associated with mental health needs, which may affect treatment experiences [48,49]. Therefore, the Stigma Scale for Receiving Psychological Help will be administered at baseline. This instrument has been validated (Cronbach α=.72) in different cultures to assess the stigma associated with receiving psychological health from mental health providers [136,137].


Sociodemographics (eg, age, sex, race or ethnicity, and education) and other relevant clinical characteristics (eg, cancer type, stage, treatment history, and time since cancer diagnosis) will be collected at baseline. The Generalized Anxiety Disorder 7-Item Scale (GAD-7) will also be administered as a diagnostic screen for generalized anxiety disorder at baseline [138]. The use of medications prescribed for anxiety (eg, antidepressants, anxiolytics, hypnotics, and sedatives) will be tracked using weekly medication diaries at weeks 0, 8, and 26.

Semistructured Interviews

It may be difficult to capture the impact of MT and CBT using quantitative measures alone. Methodological experts recommend mixed methods approaches to understand patient experiences with health interventions [139-141]. Thus, qualitative method specialists will conduct 45-minute, semistructured individual interviews over the phone at week 8 (end of treatment). To reach thematic saturation from interviews, the research team will purposively sample 30 survivors in each treatment arm to be interviewed (N=60), aiming for balance across age, sex, race, ethnicity, and treatment response. Interviews will be conducted shortly after the intervention period to enhance the recall of personal experiences during therapy sessions. Interviews will be conducted using a semistructured guide that covers the following topics: (1) acceptability: treatment satisfaction, usefulness of the intervention, barriers and facilitators to session attendance, and adherence to and engagement with between-session assignments; (2) impact on anxiety symptoms and coping: perceived benefits and harms of the intervention on anxiety and comorbid symptoms, how one copes with stress and anxiety, integration of learned skills into daily life, and plans for maintaining anxiety management skills; (3) digital literacy and experience: views toward technology, comfort level with telehealth, and transfer of acquired digital skills to other aspects of daily living; and (4) unexpected experiences: unanticipated benefits, harms, or insights from engaging with the interventions. The interview guide will include flexibility in asking probing questions to enable participants to elaborate on their experiences. All the interviews will be audio-recorded and transcribed for thematic analysis.

Analytical Approach

General Description

Analyses will be conducted using the intention-to-treat principle (ie, participants will be analyzed according to their randomly assigned treatment group regardless of dropout or treatment adherence status). Given that CBT is widely recognized as a first-line therapy for anxiety, the study team opted to test the noninferiority of MT to CBT, with the goal of understanding whether MT can be an appropriate treatment option for patients who lack CBT access or do not wish to pursue CBT. To provide complementary information for treatment decision-making, the study team will also test which intervention is superior for addressing fatigue and other symptoms co-occurring with anxiety. For all specific aims, the main analytical tool will be a linear mixed-effects model (LMM) because the primary outcome (anxiety) and secondary outcomes (fatigue, depression, insomnia, pain, cognitive dysfunction, and health-related quality of life) are repeated continuous outcomes over time [142]. This statistical procedure takes into account within-subject correlations from repeated measurements in the same subjects and allows the estimation of between-group differences without necessitating the exclusion of participants with missing data. The general template of each LMM will model the outcome as a function of the treatment arm and assessment time (categorical), controlling for the randomization stratification variables (baseline anxiety medication use, preferred language, and study site) and including a subject-specific random intercept. The general LMM template will be tailored to test the specific aim hypotheses by adding interaction terms (eg, time-by-intervention) and additional covariates of interest to the model and by reparametrizing the assessment time variable to focus on specific contrasts.

Aim 1—Hypotheses 1(a) and 1(b)

Outcome measure trajectories will be plotted by randomization arm over time. Each outcome measure will be summarized at each assessment time by treatment arm using descriptive statistics. Comparisons between randomization arms with respect to changes in symptom outcomes will be based on specific coefficients from time-by-arm interactions added to the general LMM template described above in the General Description section. Specifically, the model will include all time points (categorical) at which the outcome was assessed (baseline and weeks 4, 8, 16, and 26), and the time-by-arm interaction will include multiple coefficients corresponding to the time-by-arm interaction at each discrete postbaseline time point. However, statistical inferences will focus on the interaction coefficients at 8 and 26 weeks, which are interpreted as the differences between arms in change from baseline to 8 and 26 weeks, respectively. For hypothesis 1(a), the 2 primary end points are HADS anxiety score changes at 8 weeks and 26 weeks. On the basis of the LMM coefficients for the time-by-arm interaction at weeks 8 and 26, 2 tests will be performed to determine whether MT is noninferior to CBT at reducing the HADS anxiety subscale score within a noninferiority margin of 0.35 SD for both comparisons. The noninferiority of MT to CBT will be tested at a significance threshold of P<.025 for each comparison, controlling the overall type I error at P=.05 for the primary end point comparisons. For hypothesis 1(b), a similar LMM evaluating BFI scores over time will be used to test whether the MT arm had significantly greater improvement in fatigue (BFI score) compared with the CBT arm at weeks 8 and 26. In contrast to the primary end point noninferiority comparisons, these tests will be superiority tests. For other comorbid symptoms (eg, depression and insomnia) and quality of life, the methods described for hypothesis 1(b) will be used to compare the treatment arms. Given that a small number of cancer survivors may experience disease recurrence during the 26-week study, sensitivity analyses will be performed, excluding those individuals who experienced a recurrence during the study period. These sensitivity analyses will not replace the primary analysis.

Aim 2—Heterogeneity of Treatment Effect

An essential part of patient-centered care is recognizing that not all patients will respond to treatments the same way. As such, exploratory, hypothesis-generating heterogeneity of treatment effect (HTE) analyses will be conducted to identify patient-level factors associated with treatment response to either MT or CBT by incorporating 6 relevant variables (sex, race, ethnicity, education, outcome expectancy, and time since cancer diagnosis) and variable-by-intervention interaction terms in the mixed-effects model described above in the Aim 1—Hypotheses 1(a) and 1(b) section. These 5 exploratory LMM-based analyses will be guarded against inflated type I error owing to multiple testing by adjusting the variable-by-intervention interaction P values for the false discovery rate [143,144]. This type of subgroup analysis, although patient centered, may need to be interpreted with caution and cannot replace the primary analyses. The evaluation and reporting of HTE analyses will be based on the approach proposed by Kent et al [145]. However, recognizing other factors may also contribute to treatment responses, the study team will also apply Bayesian [146,147] and machine learning [148,149] methods, which can identify HTE and subgroups based on multiple variables simultaneously and are potentially more powerful than traditional univariate methods.

Qualitative Analyses

All interviews will be transcribed verbatim and imported into MAXQDA 2022 (VERBI Software) for analysis. The semistructured interviews will be analyzed using theoretical thematic analysis procedures as per the guidelines by Braun and Clarke [150,151]. Such an analysis is aimed at identifying patterns driven by an a priori theoretical framework or research questions. Themes will be identified using a semantic approach in which themes are derived from “the explicit meaning of the data and the analyst is not looking for anything beyond what a participant has said” [150]. Two coders will code the interview transcripts. To ensure the trustworthiness of the analysis, the primary coder will code each transcript, whereas the secondary coder will code 30% of the transcripts. Coding discrepancies will be resolved through discussion until consensus is reached. The codes will then be organized into categories, reviewed, and subsequentially organized into broader themes. Interviews from each treatment arm will be analyzed separately and then compared for convergence and divergence. The themes will be linked to patient-reported outcomes to explore whether specific themes vary according to the magnitude of change in the HADS anxiety score. To augment the HTE analyses, the study team will also explore whether qualitative themes differed by subgroup (eg, sex, race, ethnicity, education, outcome expectancy, and time since cancer diagnosis) within each treatment group.

Missing Data

As the only certain way to avoid biases from missing data is to collect complete data [152], the study team will strive to minimize missing observations by using study procedures that reduce participant burden. Participants who experience difficulties completing assessments on the web will have the option to complete assessments on paper or over the telephone with the research staff. Participants with time constraints or other barriers to completing outcome assessments will be given the option to complete only the primary outcome, that is, the HADS scale. Participants who withdraw from treatments will be encouraged to continue completing the outcome assessments. Reasons for withdrawal will be recorded for those who completely withdraw from the study. Given that missing data are inevitable in a prospective study, sensitivity analyses will be performed to assess the impact on the results of adjusting for patient disease progression or death, and analytical strategies that are as robust as possible to data losses will be applied. The study team will first explore whether missingness is associated with observed variables (eg, randomization arm and baseline outcome measures) by comparing patients with complete and incomplete data. Notably, the LMMs described previously will include patients with incomplete data under the missing at random assumption. However, the exploration of the data may deem the missing at random assumption to be inappropriate. Multiple imputation and pattern mixture models are well-established methods for addressing these issues [153,154]. Sensitivity analyses will be performed to evaluate the robustness of the LMM results by refitting the models after imputing the missing week 8 and week 26 outcomes using multiple imputation.

Sample Size and Power

The sample size is based on the primary hypothesis that MT is noninferior to CBT for anxiety reduction at weeks 8 and 26. Two tests will be performed to determine whether MT is noninferior to CBT at reducing HADS anxiety at weeks 8 and 26, each with a noninferiority margin of Cohen d=0.35 SD for both comparisons. These comparisons will be based on the LMM coefficients for the time-by-arm interaction at weeks 8 and 26; however, the power calculation is based on 2-sample t tests of differences between the arms in their change scores. Therefore, the power estimate is conservative, that is, in the LMM-based analysis, there will be slightly higher power than the power calculation presented below, with all other assumptions held constant. Assuming 150 participants are randomized to each arm, 15% attrition, and a 1-sided significance threshold of P=.025 (controlling the overall type I error at 0.05 for our 2 primary end point comparisons), there will be 80% power to find MT noninferior to CBT with respect to the HADS anxiety subscale scores at weeks 8 and 26 within a noninferiority margin of Cohen d=0.35 SD.

The noninferiority margin was informed by a large study that used multiple rigorous methods to empirically estimate a difference of 1.7 points as the MCID for the HADS scales [120]. In preliminary data from CBT recipients with baseline HADS anxiety subscale scores ≥8, the SD for the HADS anxiety subscale score was 4.2 at week 8 [110]. In addition, in a large acupuncture trial of cancer survivors, among 159 patients with baseline HADS anxiety subscale scores ≥8, the HADS anxiety subscale score SD was 4.0 during follow-up [155,156]. On the basis of these studies, it is assumed that the HADS anxiety subscale score SD will be approximately 4.2. Dividing the raw-score MCID of 1.7 points by 4.2 yields a standardized difference, that is, Cohen d=0.40. The noninferiority margin of Cohen d=0.35 SD is smaller than the standardized MCID (Cohen d=0.40) of the HADS anxiety subscale; as such, the noninferiority margin and statistical approach will find MT to be noninferior to CBT only if MT is not meaningfully worse than CBT.

Regarding the sample size for the qualitative analyses, the number of participants required to draw meaningful conclusions from semistructured interviews is determined by saturation, that is, the point at which existing themes are fully understood and no new themes emerge through further data collection [157]. Prior research has indicated that thematic saturation can typically be obtained with <15 interview participants, although these findings were based on relatively homogeneous samples [158]. Given that this study is enrolling a more heterogeneous population, the plan is to conduct 30 interviews in each treatment arm to better capture the diverse perspectives.

The MELODY study was funded by the Patient-Centered Outcomes Research Institute in July 2021. The IRB at MSK approved the study protocol in December 2021. The first study participant was enrolled in February 2022. As of January 2023, a total of 151 participants have been enrolled. Enrollment is expected to be completed by February 2024, and data collection is expected to be completed by September 2024.

Cancer survivors represent one of the fastest growing populations in the United States [1]. Unfortunately, nearly 1 in 3 survivors experience anxiety symptoms as a long-term consequence of cancer and its treatment [3,5-9]. Although pharmacological treatment options are available, polypharmacy has become a growing concern for cancer survivors [28-31]. CBT is an established first-line treatment for anxiety [32,33,159], but not all individuals respond or wish to use CBT [45,47-49]. Although MT has a growing evidence base for anxiety in cancer populations [72-75], it remains unclear how MT compares with CBT as a treatment alternative. The MELODY study will address this evidence gap by determining the comparative effectiveness of telehealth-based MT and telehealth-based CBT for anxiety in cancer survivors. This study will intentionally support enrollment access for racially and ethnically diverse populations of English- or Spanish-speaking survivors from urban, suburban, and rural settings to improve the generalizability of the findings and to inform future efforts to reduce the disparities in survivorship care. By focusing on telehealth-based interventions, this study will capitalize on the accelerating digital trends in health care and society at large, helping patients and clinicians to navigate a growing array of remotely delivered care options.

Despite its novelty and strengths, the MELODY study has some limitations. First, the study lacks a usual care or waitlist control group and therefore cannot control for Hawthorne effects or regression to the mean; however, both MT [72-75] and CBT [40,43,160] have demonstrated effectiveness for anxiety symptoms in cancer populations. Second, although the study participants are screened for the presence of generalized anxiety disorder, they do not undergo a formal diagnostic interview for psychiatric disorders. Third, this study focuses on treating anxiety symptoms rather than an anxiety disorder. However, cancer care guidelines are often based on symptom severity rather than psychiatric diagnoses [161]. Furthermore, research indicates that anxiety symptoms are associated with poor quality of life and other problematic outcomes in cancer populations, even if symptoms do not meet the criteria for an anxiety disorder [23,162]. Thus, by focusing on symptoms, this study will align closely with clinical guidelines and enroll more patients who could potentially benefit from interventions.

In conclusion, the MELODY study is the first and largest randomized clinical trial to compare the short- and long-term effectiveness of remotely delivered MT and CBT for anxiety symptoms in cancer survivors. This trial has the unique potential to produce timely findings on 2 evidence-based, scalable, and accessible interventions to promote mental well-being during cancer survivorship.


This study was funded through a Patient-Centered Outcomes Research Institute award (CER 2020C3 21044). The statements presented in this paper are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors, or the Methodology Committee. This study is also supported in part by the National Institutes of Health and National Cancer Institute Cancer Center Support Grant (P30 CA008748) and the Translational and Integrative Medicine Research Fund at the Memorial Sloan Kettering Cancer Center.

Conflicts of Interest

KMM serves as a research consultant for OncoHealth. The other authors disclose no conflicts of interest related to the content of this manuscript.

Multimedia Appendix 1

Peer-review report from PCORI Funding Announcement: Assessment of Prevention, Diagnosis, and Treatment Options - Comparative Effectiveness Research (Patient-Centered Outcomes Research Institute, USA).

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  1. Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin 2019 Sep;69(5):363-385 [FREE Full text] [CrossRef] [Medline]
  2. Mitchell AJ, Ferguson DW, Gill J, Paul J, Symonds P. Depression and anxiety in long-term cancer survivors compared with spouses and healthy controls: a systematic review and meta-analysis. Lancet Oncol 2013 Jul;14(8):721-732. [CrossRef] [Medline]
  3. Greer JA, Solis JM, Temel JS, Lennes IT, Prigerson HG, Maciejewski PK, et al. Anxiety disorders in long-term survivors of adult cancers. Psychosomatics 2011 Sep;52(5):417-423 [FREE Full text] [CrossRef] [Medline]
  4. Whitney RL, Bell JF, Bold RJ, Joseph JG. Mental health needs and service use in a national sample of adult cancer survivors in the USA: has psychosocial care improved? Psychooncology 2015 Jan 13;24(1):80-88. [CrossRef] [Medline]
  5. Mehnert A, Koch U. Psychological comorbidity and health-related quality of life and its association with awareness, utilization, and need for psychosocial support in a cancer register-based sample of long-term breast cancer survivors. J Psychosom Res 2008 Apr;64(4):383-391. [CrossRef] [Medline]
  6. Beekers N, Husson O, Mols F, van Eenbergen M, van de Poll-Franse LV. Symptoms of anxiety and depression are associated with satisfaction with information provision and internet use among 3080 cancer survivors: results of the PROFILES registry. Cancer Nurs 2015;38(5):335-342. [CrossRef] [Medline]
  7. Boyes AW, Girgis A, D'Este C, Zucca AC. Flourishing or floundering? Prevalence and correlates of anxiety and depression among a population-based sample of adult cancer survivors 6months after diagnosis. J Affect Disord 2011 Dec;135(1-3):184-192. [CrossRef] [Medline]
  8. Boyes AW, Girgis A, D'Este CA, Zucca AC, Lecathelinais C, Carey ML. Prevalence and predictors of the short-term trajectory of anxiety and depression in the first year after a cancer diagnosis: a population-based longitudinal study. J Clin Oncol 2013 Jul 20;31(21):2724-2729. [CrossRef] [Medline]
  9. Maass SW, Roorda C, Berendsen AJ, Verhaak PF, de Bock GH. The prevalence of long-term symptoms of depression and anxiety after breast cancer treatment: a systematic review. Maturitas 2015 Sep;82(1):100-108. [CrossRef] [Medline]
  10. Grov EK, Fosså SD, Dahl AA. Insomnia in elderly cancer survivors--a population-based controlled study of associations with lifestyle, morbidity, and psychosocial factors. Results from the Health Survey of North-Trøndelag County (HUNT-2). Insomnia in elderly cancer survivors. Support Care Cancer 2011 Sep 15;19(9):1319-1326. [CrossRef] [Medline]
  11. Brintzenhofe-Szoc KM, Levin TT, Li Y, Kissane DW, Zabora JR. Mixed anxiety/depression symptoms in a large cancer cohort: prevalence by cancer type. Psychosomatics 2009 Jul;50(4):383-391. [CrossRef] [Medline]
  12. Gold M, Dunn LB, Phoenix B, Paul SM, Hamolsky D, Levine JD, et al. Co-occurrence of anxiety and depressive symptoms following breast cancer surgery and its impact on quality of life. Eur J Oncol Nurs 2016 Feb;20:97-105 [FREE Full text] [CrossRef] [Medline]
  13. Shim E, Jeong D, Moon H, Noh D, Jung S, Lee E, et al. Profiles of depressive symptoms and the association with anxiety and quality of life in breast cancer survivors: a latent profile analysis. Qual Life Res 2020 Feb 18;29(2):421-429. [CrossRef] [Medline]
  14. Stone P, Richards M, A'Hern R, Hardy J. Fatigue in patients with cancers of the breast or prostate undergoing radical radiotherapy. J Pain Symptom Manage 2001 Dec;22(6):1007-1015 [FREE Full text] [CrossRef] [Medline]
  15. Geinitz H, Zimmermann FB, Thamm R, Keller M, Busch R, Molls M. Fatigue in patients with adjuvant radiation therapy for breast cancer: long-term follow-up. J Cancer Res Clin Oncol 2004 Jun 1;130(6):327-333. [CrossRef] [Medline]
  16. Dhruva A, Dodd M, Paul SM, Cooper BA, Lee K, West C, et al. Trajectories of fatigue in patients with breast cancer before, during, and after radiation therapy. Cancer Nurs 2010;33(3):201-212 [FREE Full text] [CrossRef] [Medline]
  17. Reinertsen KV, Cvancarova M, Loge JH, Edvardsen H, Wist E, Fosså SD. Predictors and course of chronic fatigue in long-term breast cancer survivors. J Cancer Surviv 2010 Dec;4(4):405-414 [FREE Full text] [CrossRef] [Medline]
  18. Haskins CB, McDowell BD, Carnahan RM, Fiedorowicz JG, Wallace RB, Smith BJ, et al. Impact of preexisting mental illness on breast cancer endocrine therapy adherence. Breast Cancer Res Treat 2019 Feb 21;174(1):197-208 [FREE Full text] [CrossRef] [Medline]
  19. Smith K, Yeruva S, Blackford A, Huang C, Westbrook K, Harding B, et al. Predictors of adherence to adjuvant endocrine therapy (ET) for early breast cancer (BC) in a prospective clinic-based cohort. Cancer Res 2018;78(4). [CrossRef]
  20. Bender CM, Gentry AL, Brufsky AM, Casillo FE, Cohen SM, Dailey MM, et al. Influence of patient and treatment factors on adherence to adjuvant endocrine therapy in breast cancer. Oncol Nurs Forum 2014 Apr 25;41(3):274-285. [CrossRef]
  21. Khushalani JS, Qin J, Cyrus J, Buchanan Lunsford N, Rim SH, Han X, et al. Systematic review of healthcare costs related to mental health conditions among cancer survivors. Expert Rev Pharmacoecon Outcomes Res 2018 Oct 20;18(5):505-517 [FREE Full text] [CrossRef] [Medline]
  22. Harris JP, Kashyap M, Humphreys JN, Pollom EL, Chang DT. The clinical and financial cost of mental disorders among elderly patients with gastrointestinal malignancies. Cancer Med 2020 Dec 06;9(23):8912-8922 [FREE Full text] [CrossRef] [Medline]
  23. Faller H, Strahl A, Richard M, Niehues C, Meng K. Symptoms of depression and anxiety as predictors of physical functioning in breast cancer patients. A prospective study using path analysis. Acta Oncologica 2017 Jun 08;56(12):1677-1681. [CrossRef]
  24. Oberoi DV, White VM, Seymour JF, Prince HM, Harrison S, Jefford M, et al. Distress and unmet needs during treatment and quality of life in early cancer survivorship: a longitudinal study of haematological cancer patients. Eur J Haematol 2017 Nov 09;99(5):423-430. [CrossRef] [Medline]
  25. Oerlemans S, Mols F, Nijziel MR, Zijlstra WP, Coebergh JW, van de Poll-Franse LV. The course of anxiety and depression for patients with Hodgkin's lymphoma or diffuse large B cell lymphoma: a longitudinal study of the PROFILES registry. J Cancer Surviv 2014 Dec;8(4):555-564. [CrossRef] [Medline]
  26. Lidgren M, Wilking N, Jönsson B, Rehnberg C. Health related quality of life in different states of breast cancer. Qual Life Res 2007 Aug 28;16(6):1073-1081. [CrossRef] [Medline]
  27. Andersen BL, DeRubeis RJ, Berman BS, Gruman J, Champion VL, Massie MJ, American Society of Clinical Oncology. Screening, assessment, and care of anxiety and depressive symptoms in adults with cancer: an American Society of Clinical Oncology guideline adaptation. J Clin Oncol 2014 May 20;32(15):1605-1619 [FREE Full text] [CrossRef] [Medline]
  28. Babcock ZR, Kogut SJ, Vyas A. Association between polypharmacy and health-related quality of life among cancer survivors in the United States. J Cancer Surviv 2020 Feb 02;14(1):89-99. [CrossRef] [Medline]
  29. Keats MR, Cui Y, DeClercq V, Grandy SA, Sweeney E, Dummer TJ. Burden of multimorbidity and polypharmacy among cancer survivors: a population-based nested case-control study. Support Care Cancer 2021 Feb 22;29(2):713-723. [CrossRef] [Medline]
  30. Murphy CC, Fullington HM, Alvarez CA, Betts AC, Lee SJ, Haggstrom DA, et al. Polypharmacy and patterns of prescription medication use among cancer survivors. Cancer 2018 Jul 01;124(13):2850-2857 [FREE Full text] [CrossRef] [Medline]
  31. Vyas A, Alghaith G, Hufstader-Gabriel M. Psychotropic polypharmacy and its association with health-related quality of life among cancer survivors in the USA: a population-level analysis. Qual Life Res 2020 Aug 23;29(8):2029-2037. [CrossRef] [Medline]
  32. Butler AC, Chapman JE, Forman EM, Beck AT. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin Psychol Rev 2006 Jan;26(1):17-31. [CrossRef] [Medline]
  33. Olatunji BO, Cisler JM, Deacon BJ. Efficacy of cognitive behavioral therapy for anxiety disorders: a review of meta-analytic findings. Psychiatr Clin North Am 2010 Sep;33(3):557-577. [CrossRef] [Medline]
  34. NCCN clinical practice guidelines in oncology: distress management. National Comprehensive Cancer Network. 2020.   URL:> [accessed 2020-12-01]
  35. Barlow D. Anxiety and Its Disorders The Nature and Treatment of Anxiety & Panic. New York, NY, United States: Guilford Publications; 1988.
  36. Pretzer J, Fleming B, Simon K. Clinical Applications of Cognitive Therapy. United States: Springer; 2013.
  37. Evidence-Based Cancer Care and Prevention Behavioral Interventions. New York, NY: Springer Publishing Company; 2003.
  38. Moorey S, Greer S. Cognitive Behaviour Therapy for People with Cancer. Oxford, England: Oxford University Press; 2002.
  39. Greer JA, Park ER, Prigerson HG, Safren SA. Tailoring cognitive-behavioral therapy to treat anxiety comorbid with advanced cancer. J Cogn Psychother 2010 Jan 01;24(4):294-313 [FREE Full text] [CrossRef] [Medline]
  40. Ye M, Du K, Zhou J, Zhou Q, Shou M, Hu B, et al. A meta-analysis of the efficacy of cognitive behavior therapy on quality of life and psychological health of breast cancer survivors and patients. Psychooncology 2018 Jul;27(7):1695-1703. [CrossRef] [Medline]
  41. Sun H, Huang H, Ji S, Chen X, Xu Y, Zhu F, et al. The efficacy of cognitive behavioral therapy to treat depression and anxiety and improve quality of life among early-stage breast cancer patients. Integr Cancer Ther 2019 Feb 22;18:1534735419829573 [FREE Full text] [CrossRef] [Medline]
  42. Getu MA, Chen C, Panpan W, Mboineki JF, Dhakal K, Du R. The effect of cognitive behavioral therapy on the quality of life of breast cancer patients: a systematic review and meta-analysis of randomized controlled trials. Qual Life Res 2021 Feb;30(2):367-384. [CrossRef] [Medline]
  43. Osborn RL, Demoncada AC, Feuerstein M. Psychosocial interventions for depression, anxiety, and quality of life in cancer survivors: meta-analyses. Int J Psychiatry Med 2006;36(1):13-34. [CrossRef] [Medline]
  44. Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One 2010 Oct 13;5(10):e13196 [FREE Full text] [CrossRef] [Medline]
  45. Beatty L, Binnion C. A systematic review of predictors of, and reasons for, adherence to online psychological interventions. Int J Behav Med 2016 Dec;23(6):776-794. [CrossRef] [Medline]
  46. Hadjistavropoulos HD, Nugent MM, Alberts NM, Staples L, Dear BF, Titov N. Transdiagnostic Internet-delivered cognitive behaviour therapy in Canada: an open trial comparing results of a specialized online clinic and nonspecialized community clinics. J Anxiety Disord 2016 Aug;42:19-29 [FREE Full text] [CrossRef] [Medline]
  47. Edmonds M, Hadjistavropoulos HD, Schneider LH, Dear BF, Titov N. Who benefits most from therapist-assisted internet-delivered cognitive behaviour therapy in clinical practice? Predictors of symptom change and dropout. J Anxiety Disord 2018 Mar;54:24-32 [FREE Full text] [CrossRef] [Medline]
  48. Eylem O, de Wit L, van Straten A, Steubl L, Melissourgaki Z, Danışman GT, et al. Stigma for common mental disorders in racial minorities and majorities a systematic review and meta-analysis. BMC Public Health 2020 Jun 08;20(1):879 [FREE Full text] [CrossRef] [Medline]
  49. McGuire TG, Miranda J. New evidence regarding racial and ethnic disparities in mental health: policy implications. Health Aff (Millwood) 2008;27(2):393-403 [FREE Full text] [CrossRef] [Medline]
  50. O'Callaghan C, Magill L. Music therapy with adults diagnosed with cancer and their families. In: The Oxford Handbook of Music Therapy. Oxford, United Kingdom: Oxford University Press; 2015.
  51. Lepore SJ. A social–cognitive processing model of emotional adjustment to cancer. In: Psychosocial Interventions for Cancer. Washington, D.C., U.S: American Psychological Association; 2001.
  52. Lepore SJ, Helgeson VS. Social constraints, intrusive thoughts, and mental health after prostate cancer. J Soc Clin Psychol 1998 Mar;17(1):89-106. [CrossRef]
  53. K Harper FW, Schmidt JE, Beacham AO, Salsman JM, Averill AJ, Graves KD, et al. The role of social cognitive processing theory and optimism in positive psychosocial and physical behavior change after cancer diagnosis and treatment. Psychooncology 2007 Jan;16(1):79-91. [CrossRef] [Medline]
  54. Koelsch S. Brain correlates of music-evoked emotions. Nat Rev Neurosci 2014 Mar;15(3):170-180. [CrossRef] [Medline]
  55. Overy K, Molnar-Szakacs I. Being together in time: musical experience and the mirror neuron system. Music Perception 2009;26(5):489-504. [CrossRef]
  56. The Social Psychology of Music. Oxford, United Kingdom: Oxford University Press; 1997.
  57. Amir D. Giving trauma a voice: the role of improvisational music therapy in exposing, dealing with and healing a traumatic experience of sexual abuse. Music Ther Perspectives 2004 Jan 01;22(2):96-103. [CrossRef]
  58. Bensimon M, Amir D, Wolf Y. Drumming through trauma: music therapy with post-traumatic soldiers. Arts Psychother 2008 Jan;35(1):34-48 [FREE Full text] [CrossRef]
  59. Koelsch S, Skouras S. Functional centrality of amygdala, striatum and hypothalamus in a "small-world" network underlying joy: an fMRI study with music. Hum Brain Mapp 2014 Jul 25;35(7):3485-3498 [FREE Full text] [CrossRef] [Medline]
  60. Finn S, Fancourt D. The biological impact of listening to music in clinical and nonclinical settings: a systematic review. Prog Brain Res 2018;237:173-200. [CrossRef] [Medline]
  61. Chanda ML, Levitin DJ. The neurochemistry of music. Trends Cogn Sci 2013 Apr;17(4):179-193. [CrossRef] [Medline]
  62. White JM. Effects of relaxing music on cardiac autonomic balance and anxiety after acute myocardial infarction. Am J Crit Care 1999 Jul;8(4):220-230. [Medline]
  63. Koelsch S, Jäncke L. Music and the heart. Eur Heart J 2015 Nov 21;36(44):3043-3049 [FREE Full text] [CrossRef] [Medline]
  64. Bradt J, Dileo C, Potvin N. Music for stress and anxiety reduction in coronary heart disease patients. Cochrane Database Syst Rev 2013 Dec 28;2013(12):CD006577 [FREE Full text] [CrossRef] [Medline]
  65. McPherson T, Berger D, Alagapan S, Fröhlich F. Active and passive rhythmic music therapy interventions differentially modulate sympathetic autonomic nervous system activity. J Music Ther 2019 Aug 13;56(3):240-264 [FREE Full text] [CrossRef] [Medline]
  66. Reeves JW, Fisher AJ, Newman MG, Granger DA. Sympathetic and hypothalamic-pituitary-adrenal asymmetry in generalized anxiety disorder. Psychophysiology 2016 Jun 02;53(6):951-957. [CrossRef] [Medline]
  67. Holwerda SW, Luehrs RE, Gremaud AL, Wooldridge NA, Stroud AK, Fiedorowicz JG, et al. Relative burst amplitude of muscle sympathetic nerve activity is an indicator of altered sympathetic outflow in chronic anxiety. J Neurophysiol 2018 Jul 01;120(1):11-22 [FREE Full text] [CrossRef] [Medline]
  68. Rauch SL, Shin LM, Wright CI. Neuroimaging studies of amygdala function in anxiety disorders. Ann N Y Acad Sci 2003 Apr;985:389-410. [CrossRef] [Medline]
  69. Tafet GE, Nemeroff CB. Pharmacological treatment of anxiety disorders: the role of the HPA axis. Front Psychiatry 2020 May 15;11:443 [FREE Full text] [CrossRef] [Medline]
  70. Mehr SA, Singh M, Knox D, Ketter DM, Pickens-Jones D, Atwood S, et al. Universality and diversity in human song. Science 2019 Nov 22;366(6468):eaax0868 [FREE Full text] [CrossRef] [Medline]
  71. Savage PE, Brown S, Sakai E, Currie TE. Statistical universals reveal the structures and functions of human music. Proc Natl Acad Sci U S A 2015 Jul 21;112(29):8987-8992 [FREE Full text] [CrossRef] [Medline]
  72. Bradt J, Dileo C, Magill L, Teague A. Music interventions for improving psychological and physical outcomes in cancer patients. Cochrane Database Syst Rev 2016 Aug 15(8):CD006911. [CrossRef] [Medline]
  73. Bro ML, Jespersen KV, Hansen JB, Vuust P, Abildgaard N, Gram J, et al. Kind of blue: a systematic review and meta-analysis of music interventions in cancer treatment. Psychooncology 2018 Feb 16;27(2):386-400. [CrossRef] [Medline]
  74. Gramaglia C, Gambaro E, Vecchi C, Licandro D, Raina G, Pisani C, et al. Outcomes of music therapy interventions in cancer patients-a review of the literature. Crit Rev Oncol Hematol 2019 Jun;138:241-254. [CrossRef] [Medline]
  75. Köhler F, Martin Z, Hertrampf R, Gäbel C, Kessler J, Ditzen B, et al. Music therapy in the psychosocial treatment of adult cancer patients: a systematic review and meta-analysis. Front Psychol 2020 Apr 16;11:651 [FREE Full text] [CrossRef] [Medline]
  76. Lyman GH, Greenlee H, Bohlke K, Bao T, DeMichele AM, Deng GE, et al. Integrative therapies during and after breast cancer treatment: ASCO endorsement of the SIO clinical practice guideline. J Clin Oncol 2018 Sep 01;36(25):2647-2655. [CrossRef] [Medline]
  77. Greenlee H, Balneaves LG, Carlson LE, Cohen M, Deng G, Hershman D, Society for Integrative Oncology. Clinical practice guidelines on the use of integrative therapies as supportive care in patients treated for breast cancer. J Natl Cancer Inst Monogr 2014 Nov;2014(50):346-358 [FREE Full text] [CrossRef] [Medline]
  78. Greenlee H, DuPont-Reyes MJ, Balneaves LG, Carlson LE, Cohen MR, Deng G, et al. Clinical practice guidelines on the evidence-based use of integrative therapies during and after breast cancer treatment. CA Cancer J Clin 2017 May 06;67(3):194-232 [FREE Full text] [CrossRef] [Medline]
  79. Bradt J, Dileo C, Myers-Coffman K, Biondo J. Music interventions for improving psychological and physical outcomes in people with cancer. Cochrane Database Syst Rev 2021 Oct 12;10(10):CD006911 [FREE Full text] [CrossRef] [Medline]
  80. Desai K, Liou K, Liang K, Seluzicki C, Mao JJ. Availability of integrative medicine therapies at national cancer institute-designated comprehensive cancer centers and community hospitals. J Altern Complement Med 2021 Nov;27(11):1011-1013 [FREE Full text] [CrossRef] [Medline]
  81. Internet/broadband fact sheet. Pew Research Center.   URL: [accessed 2020-09-14]
  82. Mobile technology and home broadband 2019. Pew Research Center.   URL: [accessed 2020-09-14]
  83. Harris J, Cheevers K, Armes J. The emerging role of digital health in monitoring and supporting people living with cancer and the consequences of its treatments. Curr Opin Support Palliat Care 2018 Sep;12(3):268-275. [CrossRef] [Medline]
  84. Ramaswamy A, Yu M, Drangsholt S, Ng E, Culligan PJ, Schlegel PN, et al. Patient satisfaction with telemedicine during the COVID-19 pandemic: retrospective cohort study. J Med Internet Res 2020 Sep 09;22(9):e20786 [FREE Full text] [CrossRef] [Medline]
  85. Roberts ET, Mehrotra A. Assessment of disparities in digital access among medicare beneficiaries and implications for telemedicine. JAMA Intern Med 2020 Oct 01;180(10):1386-1389 [FREE Full text] [CrossRef] [Medline]
  86. Vaudreuil R, Langston DG, Magee WL, Betts D, Kass S, Levy C. Implementing music therapy through telehealth: considerations for military populations. Disabil Rehabil Assist Technol 2022 Feb 01;17(2):201-210. [CrossRef] [Medline]
  87. Bronson H, Vaudreuil R, Bradt J. Music therapy treatment of active duty military: an overview of intensive outpatient and longitudinal care programs. Music Ther Perspectives 2018;36(2):195-206. [CrossRef]
  88. Levy C, Spooner H, Lee J, Sonke J, Myers K, Snow E. Telehealth-based creative arts therapy: transforming mental health and rehabilitation care for rural veterans. Arts Psychother 2018 Feb;57:20-26 [FREE Full text] [CrossRef]
  89. Lightstone AJ, Bailey SK, Voros P. Collaborative music therapy via remote video technology to reduce a veteran's symptoms of severe, chronic PTSD. Arts Health 2015 Apr 19;7(2):123-136. [CrossRef]
  90. Spooner H, Lee J, Langston D, Sonke J, Myers K, Levy C. Using distance technology to deliver the creative arts therapies to veterans: case studies in art, dance/movement and music therapy. Arts Psychother 2019 Feb;62:12-18 [FREE Full text] [CrossRef]
  91. Music Technology in Therapeutic and Health Settings. London: Jessica Kingsley Publishers; 2013.
  92. Knott D, Block S. Virtual music therapy: developing new approaches to service delivery. Music Ther Perspectives 2020;38(2):151-156. [CrossRef]
  93. Gooding L, Trainor B. Working with parents in the neonatal intensive care unit: an analysis of music therapy practice in the United States of America. Arts Psychother 2018 Jul;59:1-6 [FREE Full text] [CrossRef]
  94. Folsom S, Christie AJ, Cohen L, Lopez G. Implementing telehealth music therapy services in an integrative oncology setting: a case series. Integr Cancer Ther 2021 Oct 28;20:15347354211053647 [FREE Full text] [CrossRef] [Medline]
  95. COVID-19 resources for music therapists and students. American Music Therapy Association.   URL: [accessed 2020-09-02]
  96. Cephas AS, Sofield S, Millstein A. Embracing technological possibilities in the telehealth delivery of interactive music therapy. Nord J Music Ther 2022 Mar 20;31(3):214-227. [CrossRef] [Medline]
  97. Magee WL, Meadows T. Transitioning to teletherapy during COVID-19. Nordic J Music Ther 2022 Apr 27;31(3):199-202. [CrossRef]
  98. Andersson G. Internet-delivered psychological treatments. Annu Rev Clin Psychol 2016;12:157-179. [CrossRef] [Medline]
  99. Andrews G, Newby JM, Williams AD. Internet-delivered cognitive behavior therapy for anxiety disorders is here to stay. Curr Psychiatry Rep 2015 Jan;17(1):533. [CrossRef] [Medline]
  100. Olthuis JV, Watt MC, Bailey K, Hayden JA, Stewart SH. Therapist-supported Internet cognitive behavioural therapy for anxiety disorders in adults. Cochrane Database Syst Rev 2016 Mar 12;3(3):CD011565 [FREE Full text] [CrossRef] [Medline]
  101. Resolution on the Recognition of Psychotherapy Effectiveness. American Psychological Association. 2012 Aug.   URL: [accessed 2020-12-15]
  102. Boutron I, Moher D, Altman DG, Schulz KF, Ravaud P, CONSORT Group. Extending the CONSORT statement to randomized trials of nonpharmacologic treatment: explanation and elaboration. Ann Intern Med 2008 Feb 19;148(4):295-309 [FREE Full text] [CrossRef] [Medline]
  103. Robb SL, Burns DS, Carpenter JS. Reporting guidelines for music-based interventions. J Health Psychol 2011 Mar 13;16(2):342-352 [FREE Full text] [CrossRef] [Medline]
  104. National Institutes of Health Behavioral and Social Clinical Trials Template. National Institutes of Health.   URL: [accessed 2020-05-23]
  105. Taruffi L, Pehrs C, Skouras S, Koelsch S. Effects of sad and happy music on mind-wandering and the default mode network. Sci Rep 2017 Oct 31;7(1):14396 [FREE Full text] [CrossRef] [Medline]
  106. Bradt J, Potvin N, Kesslick A, Shim M, Radl D, Schriver E, et al. The impact of music therapy versus music medicine on psychological outcomes and pain in cancer patients: a mixed methods study. Support Care Cancer 2015 May 17;23(5):1261-1271. [CrossRef] [Medline]
  107. Yates G, Silverman M. Immediate effects of single-session music therapy on affective state in patients on a post-surgical oncology unit: a randomized effectiveness study. Arts Psychother 2015 Jul;44:57-61 [FREE Full text] [CrossRef]
  108. Trevino KM, Stern A, Hershkowitz R, Kim SY, Li Y, Lachs M, et al. Managing Anxiety from Cancer (MAC): a pilot randomized controlled trial of an anxiety intervention for older adults with cancer and their caregivers. Palliat Support Care 2021 Apr;19(2):135-145 [FREE Full text] [CrossRef] [Medline]
  109. Deshields T, Kracen A, Nanna S, Kimbro L. Psychosocial staffing at national comprehensive cancer network member institutions: data from leading cancer centers. Psychooncology 2016 Feb 11;25(2):164-169. [CrossRef] [Medline]
  110. Garland SN, Xie SX, DuHamel K, Bao T, Li Q, Barg FK, et al. Acupuncture versus cognitive behavioral therapy for insomnia in cancer survivors: a randomized clinical trial. J Natl Cancer Inst 2019 Dec 01;111(12):1323-1331 [FREE Full text] [CrossRef] [Medline]
  111. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009 Apr;42(2):377-381 [FREE Full text] [CrossRef] [Medline]
  112. Hyland KA, Hoogland AI, Gonzalez BD, Nelson AM, Lechner S, Tyson DM, et al. Evaluation of the psychometric and structural properties of the spanish version of the hospital anxiety and depression scale in latina cancer patients. J Pain Symptom Manage 2019 Aug;58(2):289-96.e2 [FREE Full text] [CrossRef] [Medline]
  113. Valenzuela J, Gning I, Irarrazaval M. Psychometric validation of the Spanish version of the Brief Fatigue Inventory [abstract]. The University of Texas MD Anderson Cancer Center, Division of Internal Medicine Research Retreat. 2012.   URL: https:/​/www.​​research/​departments-labs-institutes/​departments-divisions/​symptom-research/​symptom-assessment-tools/​brief-fatigue-inventory.​html [accessed 2023-04-02]
  114. Fernandez-Mendoza J, Rodriguez-Muñoz A, Vela-Bueno A, Olavarrieta-Bernardino S, Calhoun SL, Bixler EO, et al. The Spanish version of the insomnia severity index: a confirmatory factor analysis. Sleep Med 2012 Feb;13(2):207-210. [CrossRef] [Medline]
  115. Badia X, Muriel C, Gracia A, Núñez-Olarte JM, Perulero N, Gálvez R, Grupo Vesbpi. [Validation of the Spanish version of the Brief Pain Inventory in patients with oncological pain]. Med Clin (Barc) 2003 Jan 25;120(2):52-59. [CrossRef] [Medline]
  116. FACT-cog languages.   URL: [accessed 2021-11-01]
  117. PROMIS scale v1.2 - global health. Health Measures.   URL:> [accessed 2020-12-30]
  118. Carroll BT, Kathol RG, Noyes R, Wald TG, Clamon GH. Screening for depression and anxiety in cancer patients using the Hospital Anxiety and Depression Scale. Gen Hosp Psychiatry 1993 Mar;15(2):69-74. [CrossRef] [Medline]
  119. Smith AB, Selby PJ, Velikova G, Stark D, Wright EP, Gould A, et al. Factor analysis of the hospital anxiety and depression scale from a large cancer population. Psychol Psychother 2002 Jun;75(Pt 2):165-176. [CrossRef] [Medline]
  120. Lemay KR, Tulloch HE, Pipe AL, Reed JL. Establishing the minimal clinically important difference for the hospital anxiety and depression scale in patients with cardiovascular disease. J Cardiopulm Rehabil Prev 2019 Nov;39(6):E6-11. [CrossRef] [Medline]
  121. Bushnell MC, Ceko M, Low LA. Cognitive and emotional control of pain and its disruption in chronic pain. Nat Rev Neurosci 2013 Jul;14(7):502-511 [FREE Full text] [CrossRef] [Medline]
  122. Ferreri F, Lapp LK, Peretti C. Current research on cognitive aspects of anxiety disorders. Curr Opin Psychiatry 2011 Jan;24(1):49-54. [CrossRef] [Medline]
  123. Mendoza TR, Wang XS, Cleeland CS, Morrissey M, Johnson BA, Wendt JK, et al. The rapid assessment of fatigue severity in cancer patients: use of the Brief Fatigue Inventory. Cancer 1999 Mar 01;85(5):1186-1196. [CrossRef] [Medline]
  124. Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 2011 May 01;34(5):601-608 [FREE Full text] [CrossRef] [Medline]
  125. Savard M, Savard J, Simard S, Ivers H. Empirical validation of the Insomnia Severity Index in cancer patients. Psychooncology 2005 Jun;14(6):429-441. [CrossRef] [Medline]
  126. Cleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. Ann Acad Med Singap 1994 Mar;23(2):129-138. [Medline]
  127. Bell ML, Dhillon HM, Bray VJ, Vardy JL. Important differences and meaningful changes for the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog). J Patient Rep Outcomes 2018 Oct 12;2(1):48. [CrossRef]
  128. Hays RD, Bjorner JB, Revicki DA, Spritzer KL, Cella D. Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Qual Life Res 2009 Sep;18(7):873-880 [FREE Full text] [CrossRef] [Medline]
  129. Bauml J, Xie SX, Farrar JT, Bowman MA, Li SQ, Bruner D, et al. Expectancy in real and sham electroacupuncture: does believing make it so? J Natl Cancer Inst Monogr 2014 Nov;2014(50):302-307 [FREE Full text] [CrossRef] [Medline]
  130. Mao JJ, Armstrong K, Farrar JT, Bowman MA. Acupuncture expectancy scale: development and preliminary validation in China. Explore (NY) 2007 Jul;3(4):372-377 [FREE Full text] [CrossRef] [Medline]
  131. Mao JJ, Xie SX, Bowman MA. Uncovering the expectancy effect: the validation of the acupuncture expectancy scale. Altern Ther Health Med 2010;16(6):22-27 [FREE Full text] [Medline]
  132. Keefe JR, Amsterdam J, Li QS, Soeller I, DeRubeis R, Mao JJ. Specific expectancies are associated with symptomatic outcomes and side effect burden in a trial of chamomile extract for generalized anxiety disorder. J Psychiatr Res 2017 Jan;84:90-97 [FREE Full text] [CrossRef] [Medline]
  133. Beasley MJ, Ferguson-Jones EA, Macfarlane GJ. Treatment expectations but not preference affect outcome in a trial of CBT and exercise for pain. Can J Pain 2017;1(1):161-170 [FREE Full text] [CrossRef] [Medline]
  134. Liou KT, Trevino KM, Meghani SH, Li QS, Deng G, Korenstein D, et al. Fear of analgesic side effects predicts preference for acupuncture: a cross-sectional study of cancer patients with pain in the USA. Support Care Cancer 2021 Jan 08;29(1):427-435 [FREE Full text] [CrossRef] [Medline]
  135. Mas-Herrero E, Marco-Pallares J, Lorenzo-Seva U, Zatorre R, Rodriguez-Fornells A. Individual differences in music reward experiences. Music Perception 2013;31(2):118-138. [CrossRef]
  136. Zhou Y, Lemmer G, Xu J, Rief W. Cross-cultural measurement invariance of scales assessing stigma and attitude to seeking professional psychological help. Front Psychol 2019 May 31;10:1249 [FREE Full text] [CrossRef] [Medline]
  137. Komiya N, Good GE, Sherrod NB. Emotional openness as a predictor of college students' attitudes toward seeking psychological help. J Counsel Psychol 2000 Jan;47(1):138-143. [CrossRef]
  138. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006 May 22;166(10):1092-1097. [CrossRef] [Medline]
  139. Bradt J, Burns DS, Creswell JW. Mixed methods research in music therapy research. J Music Ther 2013 Jun 01;50(2):123-148. [CrossRef] [Medline]
  140. Potvin N, Bradt J, Kesslick A. Expanding perspective on music therapy for symptom management in cancer care. J Music Ther 2015 Mar 09;52(1):135-167. [CrossRef] [Medline]
  141. Cox A, Lucas G, Marcu A, Piano M, Grosvenor W, Mold F, et al. Cancer survivors' experience with telehealth: a systematic review and thematic synthesis. J Med Internet Res 2017 Jan 09;19(1):e11 [FREE Full text] [CrossRef] [Medline]
  142. Tango T. Power and sample size for the S:T repeated measures design combined with a linear mixed-effects model allowing for missing data. J Biopharm Stat 2017 Mar 20;27(6):963-974. [CrossRef] [Medline]
  143. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Society Series B (Methodological) 2018 Dec 05;57(1):289-300. [CrossRef]
  144. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Statist 2001 Aug 1;29(4):1165-1188. [CrossRef]
  145. Kent DM, Rothwell PM, Ioannidis JPA, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials 2010 Aug 12;11:85 [FREE Full text] [CrossRef] [Medline]
  146. Henderson NC, Louis TA, Wang C, Varadhan R. Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research. Health Serv Outcomes Res Methodol 2016 Sep 20;16(4):213-233 [FREE Full text] [CrossRef] [Medline]
  147. Wang C, Louis TA, Henderson NC, Weiss CO, Varadhan R. Beanz: an r package for bayesian analysis of heterogeneous treatment effects with a graphical user interface. J Stat Soft 2018;85(7):1-31. [CrossRef]
  148. Imai K, Ratkovic M. Estimating treatment effect heterogeneity in randomized program evaluation. Ann Appl Stat 2013 Mar 1;7(1):443-470. [CrossRef]
  149. Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc 2018 Jun 06;113(523):1228-1242. [CrossRef]
  150. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006 Jan;3(2):77-101. [CrossRef]
  151. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qual Res Sport Exercise Health 2019 Jun 13;11(4):589-597. [CrossRef]
  152. Little RJ, D'Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med 2012 Oct 04;367(14):1355-1360 [FREE Full text] [CrossRef] [Medline]
  153. Siddique J, Harel O, Crespi CM. Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial. Ann Appl Stat 2012 Dec 1;6(4):1814-1837. [CrossRef]
  154. Ibrahim JG, Molenberghs G. Missing data methods in longitudinal studies: a review. Test (Madr) 2009 May 01;18(1):1-43 [FREE Full text] [CrossRef] [Medline]
  155. Mao JJ, Liou K, Panageas K, Baser RE, Romero SA, Li QS, et al. Effects of electroacupuncture and auricular acupuncture for chronic pain in cancer survivors: the PEACE randomized controlled trial. J Clin Oncol 2020 May 20;38(15_suppl):12004. [CrossRef]
  156. Liou KT, Baser R, Romero SA, Green J, Li QS, Orlow I, et al. Personalized electro-acupuncture versus auricular-acupuncture comparative effectiveness (PEACE): a protocol of a randomized controlled trial for chronic musculoskeletal pain in cancer survivors. Medicine (Baltimore) 2020 May 22;99(21):e20085 [FREE Full text] [CrossRef] [Medline]
  157. Bowen GA. Naturalistic inquiry and the saturation concept: a research note. Qual Res 2008 Feb 01;8(1):137-152. [CrossRef]
  158. Guest G, Bunce A, Johnson L. How many interviews are enough?: an experiment with data saturation and variability. Field Methods 2016 Jul 21;18(1):59-82. [CrossRef]
  159. Otto MW, Smits JA, Reese HE. Cognitive-behavioral therapy for the treatment of anxiety disorders. J Clin Psychiatry 2004;65 Suppl 5:34-41. [Medline]
  160. Moyer A, Sohl SJ, Knapp-Oliver SK, Schneider S. Characteristics and methodological quality of 25 years of research investigating psychosocial interventions for cancer patients. Cancer Treat Rev 2009 Aug;35(5):475-484 [FREE Full text] [CrossRef] [Medline]
  161. Optimal resources for cancer care (2020 standards): educational video series. American College of Surgeons.   URL: [accessed 2020-12-21]
  162. Chow S, Wan BA, Pidduck W, Zhang L, DeAngelis C, Chan S, et al. Symptoms predictive of overall quality of life using the edmonton symptom assessment scale in breast cancer patients receiving radiotherapy. Clin Breast Cancer 2019 Dec;19(6):405-410. [CrossRef] [Medline]

ASCO: American Society of Clinical Oncology
BFI: Brief Fatigue Inventory
BPI: Brief Pain Inventory
CBT: cognitive behavioral therapy
CONSORT: Consolidated Standards of Reporting Trials
FACT-Cog: Functional Assessment of Cancer Therapy–Cognitive Function
GAD-7: Generalized Anxiety Disorder 7-Item Scale
HADS: Hospital Anxiety and Depression Scale
HIPAA: Health Insurance Portability and Accountability Act
HTE: heterogeneity of treatment effect
IRB: institutional review board
LMM: linear mixed-effects model
MCI: Miami Cancer Institute
MCID: minimal clinically important difference
MELODY: Music Therapy Versus Cognitive Behavioral Therapy for Cancer-related Anxiety
METE: Mao Expectancy of Treatment Effects
MSK: Memorial Sloan Kettering Cancer Center
MT: music therapy
NCCN: National Comprehensive Cancer Network
NCI: National Cancer Institute
PROMIS–Global Health: Patient-Reported Outcomes Measurement Information System–Global Health
REDCap: Research Electronic Data Capture

Edited by T Leung; The proposal for this study was peer reviewed by the Patient-Centered Outcomes Research Institute (PCORI), USA, as part of the PCORI Funding Announcement: Assessment of Prevention, Diagnosis, and Treatment Options - Comparative Effectiveness Research. See the Multimedia Appendix for the peer-review report;submitted 06.02.23; accepted 19.02.23; published 27.04.23


©Kevin T Liou, Kelly M McConnell, M Beatriz Currier, Raymond E Baser, Jodi MacLeod, Desiree Walker, Camila Casaw, Greta Wong, Lauren Piulson, Karen Popkin, Ana Maria Lopez, Katherine Panageas, Joke Bradt, Jun J Mao. Originally published in JMIR Research Protocols (, 27.04.2023.

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