In the past decade, the amount of research into the effects of the arts on health and well-being has increased. Nonpharmacologic approaches, such as music, continue to be explored for the treatment and symptom management of brain disorders of aging, including stroke, Parkinson disease (PD), Alzheimer disease (AD), and AD-related dementias (ADRDs). There is evidence to support that music engages many different areas of the brain and may aid to strengthen brain networks and pathways involved in sensory and motor processes, emotion, affect, and memory.^e1 Given that many of these domains can be affected by brain disorders of aging, music may therefore represent a cheaper, less invasive, and more accessible therapeutic avenue than traditional pharmacologic approaches.

Significant strides have been made over the past decade to further the understanding, development, and effectiveness of MBIs for various disorders. For example, rhythmic auditory stimulation (RAS), a neurologic music therapy that involves the presentation of auditory rhythmic cues, has shown great promise for the treatment of gait disorders in individuals living with PD. RAS may reduce the number of freezing episodes and number of falls in these patients. In addition, studies indicate that RAS may also provide some benefits to individuals with other neurologic conditions where gait and postural stability are affected, such as stroke, traumatic brain injury, and AD. Moreover, singing can improve respiratory control and strengthening of muscles associated with swallowing and gait. Neurologic music therapy and melodic intonation therapy are used successfully for the rehabilitation of patients with nonfluent aphasia by stimulating their cognitive, emotional, and sensorimotor functions and by increasing their expressive language scores (e.g., repetition, sentence completion, and naming nouns).^e2 Other studies have explored whether MBIs could improve cognitive function in healthy aging and AD/ADRD, as well as address the behavioral and psychological symptoms of AD (e.g., aggression, anxiety, irritability, and depression).

Although MBIs have shown promise for symptom management in brain disorders of aging, large-scale, rigorous, well-designed, and well-powered studies are needed to fully understand how music affects the brain and its therapeutic potential for these conditions. In the past 5 years, 2 Cochrane systematic reviews have concluded that MBIs have demonstrated benefits for people living with dementia and for people with acquired brain injury; however, these reviews have also highlighted the need for high-quality randomized trials before recommendations can be made for clinical practice. A more recent systematic review of MBIs for community-dwelling people living with dementia noted that inconsistency in study designs, procedures, and measures prevented specific conclusions to be drawn about potential therapeutic benefit.

A major limitation to widespread application of MBIs has been the scarcity of data from rigorous, well-powered studies. Reports of the beneficial effects of MBIs have emerged either from anecdotal evidence or from small-scale clinical trials.²¹ To further illustrate this point, the Agency for Healthcare Research and Quality (AHRQ) and the National Academies of Sciences, Engineering, and Medicine (NASEM) conducted a literature review in 2017 to assess the state of the science regarding nonpharmacologic approaches, including music, that might benefit the quality of life for people living with dementia. Their 2020 report concluded from the 35 studies examined that evidence was insufficient to draw conclusions about the benefits of music therapy for agitation, anxiety, depression, mood, and quality of life for people living with dementia. Moreover, many of these studies had not been designed within a scientific and theoretical framework, and their results remain preliminary.

Another challenge for the broad implementation of MBIs is the lack of consistent descriptive terminology. MBIs are divided into 2 major categories: music therapy and music medicine. Music therapy is an established health profession in which music is used within a therapeutic relationship to address physical, emotional, cognitive, and social needs of individuals and includes the triad of music, clients, and qualified credentialed music therapists. By contrast, music medicine is defined as having patients listen to prerecorded or live music, which is often managed by a medical professional other than a music therapist, such that the music plays the role of a medicine. Notably, unlike music therapy, music medicine does not require a therapeutic relationship with the patient. A clear distinction between these 2 types of MBIs is important for assessing treatment response and functional outcomes.

Harnessing the therapeutic potential of music is of wide interest across the NIH (21 of NIH's 27 institutes and centers have representatives to the Trans-NIH Music and Health Working Group (WG), part of the Sound Health Initiative). For MBIs to fulfill their potential, they must become more rigorous and replicable and align with the NIH Rigor and Reproducibility Policy, which will require the development of standards and tools that can be applied to interventional studies. The NIH, in partnership with the Renée Fleming Foundation and the Foundation for the NIH (FNIH), convened 3 workshops in 2021 to gather diverse and unbiased perspectives from 5 different communities representing content experts and a broad range of stakeholders (see eAppendix 1, links.lww.com/WNL/C605, for specific information on panel composition and discussion topics for each workshop). A direct outcome of these workshops is the development of the NIH MBI Toolkit: a set of guidelines and recommendations on what components need to be included in an MBI study to enhance data collection; allow for rigor, replicability, cross-study comparison, and interpretation; and advance the biomedical research enterprise (Table 1). Other health research fields (e.g., physical therapy and traumatic brain injury) have benefited from such development of standards for interventional studies.

Development of the NIH MBI Toolkit: Methodology and Approach

In preparation for the NIH-FNIH workshop series, a planning committee consisting of 12 NIH staff—Program Directors experienced in scientific program development and a subset of the larger Trans-NIH Music and Health WG—was established. Through an iterative and democratic process, and with input from the NIH Director and the Trans-NIH Music and Health WG, the planning committee members met biweekly to reach consensus on the workshop format, the selection of the members for the external expert panel, and the development of the workshop agenda.

To fully assess the state of the music and health research field, we conducted a broad and comprehensive literature and database reviews of randomized controlled trial MBI studies through PubMed and other appropriate sources (Embase, Web of Science, PsycINFO, ClinicalTrials.gov, and International Clinical Trials Registry Platform). We augmented the results of our searches by including publications cited by workshop content experts and other NIH investigators in the relevant fields of neuroscience, music therapy and music medicine, behavioral intervention development, clinical trial methodology, and patient arts and advocacy.

To meet our goals of providing the research community with guidelines and recommendations on MBI development, it was critical to identify panelists with expertise in areas of direct relevance and to also bring together a truly interdisciplinary panel. For each workshop, the panel included 5 or 6 experts representing the following disciplines: neuroscience, music therapy and music medicine, behavioral intervention development, clinical trial methodology, and patient and arts advocacy. These experts were identified and selected based on their published body of work, participation at other scientific meetings, and recommendations from NIH staff and external stakeholders. The NIH planning committee was especially mindful to have an expert panel that included both scientists actively working in the field of music and health and experts in a relevant discipline but not directly involved in MBI research, thus bringing fresh and unbiased perspectives to the panel.

Before each workshop, panelists worked closely with the NIH planning committee and engaged in 2 or 3 premeetings to allow for in-depth discussions of relevant questions that were provided to them in advance. Each workshop was organized around a specific theme, framed by a keynote address, and followed by a moderated discussion led by Alan Weil, Editor-in-Chief of the health policy journal, Health Affairs. This approach was adopted to promote rich discussions, diversity of opinions from the interdisciplinary panelists, and comments from a general audience composed of basic and clinical scientists, music professionals, staff from the NIH and other federal agencies, and the public.

A unique feature of this process was the presentation of demonstration projects by 2 interdisciplinary subgroups drawn from the pool of panelists. These subgroups were tasked with developing an MBI for a particular disease or condition, applying the guiding principles that were developed throughout the 3-workshop series (see eAppendix 1 for descriptions of 2 prototype MBI studies, links.lww.com/WNL/C605). To further inform the development of the Toolkit, additional feedback and input were also gathered from the external community through a formal request for information (eAppendix 1).

Notably, the overall goal of developing the NIH MBI Toolkit is to provide standards and tools to investigators seeking NIH funding for their MBI studies, and as such, many of the selected panelists were based in the United States. The lack of representation from a cadre of international researchers engaged in music and health research may represent a potential limitation of the development process.

Essential Components of the NIH MBI Toolkit

The NIH MBI Toolkit was conceptualized to promote incorporation in MBI studies of the following elements: (1) a conceptual model or framework to guide the design of the intervention, (2) a clear research question and the provision of supporting data to test the posited hypothesis, (3) a core set of common data elements or building blocks that must be included in every MBI, (4) a comprehensive description of the intervention, (5) a detailed protocol for delivering the intervention, (6) the population to be studied, and (7) control groups or comparators.

Conceptual Models or Frameworks for MBIs

A first step in testing an MBI is developing a conceptual framework for the proposed intervention, that is, a representation of an expected relationship(s) between or among variables (i.e., the hypothesis). As in the evidence-based medicine PICOS model (population, intervention, comparison, outcome, and study design),^e4 the choice of a framework depends on the targeted population, specific intervention, comparison group, outcome measures, and research question, as well as the research stage and study design (e.g., static, mixed methods, or adaptive). Below are 4 examples of models or frameworks (behavioral/experimental medicine, music therapy, neural, and resilience/social science) that can be used in MBI research.

Experimental Medicine Framework

The NIH Science of Behavior Change (SOBC) program illustrates the experimental medicine approach to identifying and testing hypothesized mechanisms of action of therapies at each stage of intervention development, with the overall goal of improving the understanding of mechanisms underlying human behavior change. In SOBC, the focus is on defining the processes or mechanisms that are driving the behavior change or effect of an intervention, verifying the intervention target mechanism, and identifying outcome measures of target engagement. For example, in an MBI, the SOBC theoretical model would highlight the components of music that are likely to influence the potential processes or mechanisms (e.g., cognitive, emotional, or physiologic) that, in the context of a specific study, result in changes in the outcome of interest (e.g., cognitive, emotional, or functional outcomes).

Music Therapy Framework

Many factors influence the guiding framework for music therapists, including the clinical setting, client population, client age, diagnosis, and theoretical orientations of the therapist. Music therapists use a combination of various types of frameworks to address the client's needs, including psychodynamic, humanistic, behavioral, and music-centered approaches.^e5,e6 For example, therapists may use a humanistic approach to facilitate respect, trust, and engagement with their clients and further incorporate a behavioral approach that uses music as a cue to redirect the focus of attention to obtain a desired response. An important consideration for determining which approaches are most appropriate is ensuring that all clients' needs will be addressed by the chosen combination.

Neuromechanistic Framework

The neuromechanistic framework requires that interventional studies provide a mechanistic conceptual grounding that builds connections between clinical and basic research. Starting from the evidence that music engages many neural systems, including the perception, sensory-motor, memory, attention, and emotion/reward systems (Figure 1), MBIs for a given disorder should consider the alignment of the neural subsystems involved in both the disorder and the intervention. For example, music's ability to recruit the reward system could be exploited for conditions with motivational problems or anhedonia.

Figure 1. Pathways Underlying Neural and Physiologic Responses to Music.

_{Adapted with permission from Koelsch S. Brain correlates of music-evoked emotions. Nat Rev Neurosci. 2014; 15:170-180. doi.org/10.1038/nrn3666}

Resilience Framework

The resilience framework uses a contextual support model of music therapy that is based on a motivational theory of coping. The framework argues that therapeutic music environments possess structural elements that support autonomy, encourage the freedom of expression, and promote interaction of patients with their environment. Music engagement is then used to reduce stressful conditions or mitigate risk situations.^,e7

Research Question and Supporting Data for MBI Hypothesis Testing

MBI research should be built on evidence from prior studies, extant literature, and/or clinical practice. Music and health researchers can benefit from the experience of researchers in behavioral intervention development, which highlights the importance of systematic reviews and the need to use basic research to inform selection of theoretical models while maintaining clinical equipoise. Furthermore, the initial literature review should also include a focus on components of the intervention itself—for MBIs, this focus would include musical elements, such as frequency, tempo, melody, and playback volume, as well as nonmusical intervention components (e.g., imagery that accompanies the music), which are important to engage hypothesized mechanisms or produce the desired outcomes.

MBI Building Blocks and Common Data Elements

The essential components of an MBI include the intervention itself; the mode of delivery; the target population; a study design, which distinguishes the MBI's effects from those of socialization during the experience; and data collection and management. Figure 2 provides examples of various building blocks that should be incorporated in the planning and implementation of MBI studies.

Figure 2. Examples of Building Blocks for MBIs.

Intervention

When defining the intervention, investigators should consider specific elements of music (e.g., pitch, timbre, or rhythm), modes of engagement such as actively performing and creating music or passively listening to music, the relationship between culturally specific music preferences and outcomes, patient demographics, and contextual factors. In the earliest stage of the research, parameters such as dose and frequency should be treated as experimental variables to be manipulated to enhance efficacy. Furthermore, longer, lower intensity interventions, and/or the inclusion of booster sessions may be important to maintain the desired treatment response. In this paper, we define the building blocks of the MBI in the context of a research setting—the rationale, clinical population, and the aim of the study will be determined by the research question.

Protocol Delivery

A systematic review of music intervention trials reported that less than half of those studies examined treatment fidelity (i.e., the extent to which an intervention is implemented consistently across practitioners and trial sites). Similar findings were noted in the 2020 AHRQ/NASEM report. Critical considerations about the delivery methods for MBI studies include: (1) music characteristics (e.g., live vs recorded, music genre, personalized vs nonpersonalized, and patient selected vs investigator selected); (2) research setting (e.g., laboratory, clinic, or community); (3) mode of participation (e.g., individual vs group, in-person vs remote delivery); (4) timescale (e.g., short vs long term, session length and intensity, follow-up frequency, or potential for habituation); (5) staffing and support (e.g., expert therapists vs wide range of providers with appropriate training); (6) resources and organization (e.g., cost, scheduling, or assessments); and (7) feasibility, accessibility, and adherence. These considerations are equally important to objectively establish the intensity of the intervention needed to observe clinical improvements and maximize dissemination and implementation of the intervention.

Study Population

The study population is the subset of the target population available for a study (e.g., individuals diagnosed with early-onset dementia from a group of nursing home residents). MBI investigators must have a strong rationale for testing a specific intervention in any given population, for example, the choice of the target population might focus on disorder subtypes or severity, ability to conduct convenience sampling in a pilot study, availability of a control group in a randomized trial, or the relative importance of studying time courses longitudinally. Additional pragmatic considerations include accessibility to the population, its stability over time, and the resources available in the setting where the intervention will be tested. In addition, screening of patients to eliminate confounding variables, such as hearing loss or amusia, is advisable.

Control Groups or Comparators

Although control groups or comparators may not always be needed for early-phase research, they are crucial for randomized controlled trials. The goals of a study and the research question should drive the selection of the comparator group. Devising the appropriate control condition for music studies is complex, and current MBI research has poorly designed control conditions. For MBI studies, control conditions may include a music element such as a slowed-down version of the same music, meaningless sounds, or nature sounds or, instead of a music element, the control may be an audiobook. The investigative team needs to consider whether the chosen control condition is intended to control for intervention effects that are unrelated to the music (e.g., attention, sound stimulation, visual stimulation, and shared experience) or for effects of the specific intervention protocol delivery method (e.g., recorded music listening vs guided or tailored music listening). Moreover, control conditions must match the test intervention in intensity of engagement and observation (controlling for the Hawthorne effect). Equally important is controlling for a possible placebo effect by matching the test intervention in the intensity of the treatment delivered.

The optimal comparator is one that will provide the clearest answer to the primary research question or the strongest test of the trial's primary hypothesis. The rationale for the comparator choice should focus on the primary purpose of the trial and not be weakened by lesser considerations or arbitrary rules. An NIH expert panel issued a useful framework for considering and justifying control groups with the Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials.

Potential Outcome Measures and Biomarkers: Examples for Brain Disorders of Aging MBIs

MBIs for brain disorders of aging, including AD/ADRD, PD, and stroke, provide some of the most compelling evidence for music's health benefit and create a model for future work across the lifespan. The NIH MBI Toolkit, developed through discussions with our expert panels, suggests core datasets of outcome measures and biomarkers for brain disorders of aging that researchers may select for their MBI studies. Some guiding principles and factors that merit consideration when selecting these outcome measures and biomarkers are listed in Table 2.

Mechanistic and Clinical Outcome Measures

The essential steps in developing hypotheses on the impact of MBIs for brain disorders of aging include adopting a conceptual framework for the outcomes to be measured; choosing the appropriate study design; identifying relevant domains for proximal (short-term) and distal (long-term) outcomes, boundary conditions (i.e., moderators), and mechanisms (i.e., mediators); and determining the relevant biomarkers. For each domain, a range of measurement modalities is possible, including self-report, performance-based measures, direct observation, sensor technology measures, physiologic monitoring, and various functional brain measures, each of which has strengths and weaknesses for assessing the domain of interest. Therefore, multimodal assessment of a given domain is often preferable (Table 3).

Mechanistic and clinical outcomes may be derived from studies using an intervention in healthy subjects or individuals with a specific disease/condition to better understand the clinical aspects of human biology and/or disease. More specifically, mechanistic outcomes provide insights into biological or behavioral processes, the pathophysiology of a disease, or an intervention's mechanism of action. Clinical outcomes are measurable changes from an objective baseline in health, function, or quality of life that results from a treatment or intervention.

Biomarkers

A biomarker is a defined characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, a pathogenic process, or pharmacologic responses to a therapeutic intervention. Biomarkers can be used to parse out the causally active components of an intervention and provide insights not just into whether an intervention works but also how it works. Biomarkers can indicate susceptibility or risk; predict or measure a condition or response; assess safety; or be used for diagnosis, prognosis, or monitoring.

Brain disorders of aging are complex conditions affecting numerous biological, behavioral, and cognitive-affective systems. As such, a wide array of biomarkers can be considered when designing MBIs for brain disorders of aging, including inflammation, brain structure, neurologic functioning, gene expression, affect, sensory and motor activity, stress markers (e.g., galvanic skin response, pupillometry, and cortisol) (Table 4).

Policy Implications and Recommendations

Advancement in science is predicated on 2 critical concepts: rigor in designing and performing scientific research and the ability to reproduce biomedical research findings. In 2016, the NIH announced the Rigor and Reproducibility Policy to better ensure that researchers use unbiased and well-controlled experimental design, methodology, analysis, interpretation, and reporting of results. Furthermore, the policy encourages scientific integrity, public accountability, and social responsibility.

MBIs are a valuable therapeutic opportunity because these interventions are low cost, mostly devoid of side effects, often scalable in health care systems, and generally well accepted by patients. The development and dissemination of the NIH MBI Toolkit addresses a pressing need in the music and health field for enhanced data collection, with guidelines for scientifically rigorous studies, representing a necessary step in accelerating progress toward incorporating MBIs in health care systems.

Rigorous MBI research also requires a team science approach, bringing together different groups with varied expertise, perspectives, and ideas. To be successful, the investigative team should be interdisciplinary and have expertise in the population or health condition of interest, intervention development, target outcomes (e.g., functional, biological, disease, and nondisease outcomes), neuroscience, and relevant biomarkers. The team should include a methodologic expert(s); statistician; competent clinician(s) to deliver the intervention; stakeholders (e.g., patients or caregivers); skilled study coordinator(s) to supervise recruitment, data collection, adherence to the study protocol, and data management; and an individual with experience managing funded research. Utilization of the guiding principles of the NIH MBI Toolkit is strongly recommended for NIH-funded studies of MBIs.

Final Points and Next Steps for MBIs

MBIs have the potential to influence patient-relevant target outcomes, such as manage symptoms, slow disease progression, rehabilitate, and improve quality of life in many disease conditions across the lifespan. A critical step toward incorporating MBIs into US health care systems is the dissemination and implementation of the NIH MBI Toolkit's guiding principles for large-scale, rigorous, and replicable evidence-based research. Table 5 outlines other key take-home messages from this manuscript.

Development of the NIH MBI Toolkit has highlighted the utility of various technologic advances in behavioral measurement that are enabling more rigorous and robust measurements to be obtained. Among these advances are the Item Response Theory and computer adaptive testing, which are at the core of both the Patient-Reported Outcomes Measurement Information System (PROMIS) and the NIH Toolbox. These systems are relevant across conditions and were developed and evaluated using state-of-the-science psychometric methods. The NIH Toolbox, which has more than 100 standalone measures including many cognition measures, is particularly useful. Aspects of the NIH Toolbox and PROMIS can complement the NIH MBI Toolkit and are freely available, although technology fees are required when tests are administered digitally.

Other advances include Ecological Momentary Assessment (EMA), which involves intensive sampling over time, often obtained through smartphones or text messaging (rather than retrospective self-report) and passive sensor technologies (e.g., smartphones, wearable sensors, or home-based sensors). EMA can also leverage advances in neuroscientific tools, which provide very dynamic and detailed pictures of neurocircuitry and how it changes over time.

In addition, the Biomarkers, EndpointS, and other Tools Resource glossary, developed by the US Food and Drug Administration and the NIH, clarifies terminology and uses of biomarkers and endpoints, as they pertain to progression from basic biomedical research to medical product development to clinical care. This resource creates unique opportunities for novel biomarker development specific to the music and health field. For example, auditory biomarkers derived from an EEG and the auditory frequency-following response could be used to screen or stratify cohorts in an MBI study. Other useful tools and measures include psychometrically sound and validated measures of music engagement, flow, creativity, and joy. Digital measures of facial expressions and movement, as well as innovative personalized music delivery systems, would further enhance the ecologic validity of MBI protocols.

Finally, the NIH foresees potential research opportunities for future modification and updating of the NIH MBI Toolkit relevant to various diseases and conditions across the lifespan. The research community is strongly encouraged to take advantage of this Toolkit to help improve the rigor and replicability of MBIs

In the past decade, research into the effects of the arts on health and well-being has increased. Non-drug approaches, such as music, continue to be explored for treating and managing symptoms of brain disorders in older adults, including stroke, Parkinson's disease (PD), Alzheimer's disease (AD), and related dementias. Evidence suggests that music engages many brain areas, potentially strengthening networks involved in sensory and motor processes, emotion, and memory. Since many of these areas are affected by brain disorders of aging, music may offer a more affordable, less invasive, and more accessible therapy than traditional drug treatments.

Significant progress has been made in understanding and developing music-based interventions (MBIs) for various disorders. For example, rhythmic auditory stimulation (RAS), a type of neurologic music therapy using rhythmic sound cues, shows promise for gait disorders in individuals with PD. RAS may reduce freezing episodes and falls in these patients. Studies also indicate that RAS can benefit individuals with other neurologic conditions affecting gait and stability, such as stroke, traumatic brain injury, and AD. Additionally, singing can improve breathing control and strengthen muscles involved in swallowing and walking. Neurologic music therapy and melodic intonation therapy are successfully used to help patients with nonfluent aphasia by stimulating cognitive, emotional, and motor functions, and by improving expressive language. Other studies have investigated if MBIs could enhance cognitive function in healthy aging and AD/ADRD, and address behavioral and psychological symptoms of AD, such as aggression, anxiety, and depression.

Despite the promise of MBIs for managing symptoms in brain disorders of aging, large, well-designed studies are still needed to fully understand how music affects the brain and its therapeutic potential. Recent systematic reviews have shown benefits for people with dementia and acquired brain injury but also emphasized the need for high-quality randomized trials before clinical recommendations can be made. A more recent review of MBIs for people with dementia living in the community noted that inconsistent study designs and measurements prevented drawing specific conclusions about therapeutic benefits. A major limitation to the widespread use of MBIs has been the lack of data from rigorous, well-powered studies. Reports of beneficial effects often come from individual stories or small trials. For instance, a 2020 report found insufficient evidence from 35 studies to conclude that music therapy benefits agitation, anxiety, depression, mood, and quality of life for people with dementia. Many of these studies also lacked a strong scientific framework, making their results preliminary.

Another challenge for implementing MBIs widely is the lack of consistent terminology. MBIs are generally divided into two main categories: music therapy and music medicine. Music therapy is an established health profession where music is used within a therapeutic relationship to address individuals' physical, emotional, cognitive, and social needs, involving music, clients, and qualified music therapists. In contrast, music medicine involves patients listening to pre-recorded or live music, often managed by a medical professional other than a music therapist, where music serves a role similar to medicine. Unlike music therapy, music medicine does not require a therapeutic relationship. A clear distinction between these two MBI types is important for accurately assessing treatment responses and outcomes. The National Institutes of Health (NIH) has a broad interest in harnessing music's therapeutic potential. For MBIs to reach their full potential, research must become more rigorous and repeatable, aligning with the NIH Rigor and Reproducibility Policy, which requires developing standards and tools for intervention studies. The NIH, in partnership with other foundations, held three workshops in 2021 to gather diverse perspectives from experts and stakeholders. A direct result of these workshops is the NIH MBI Toolkit, a set of guidelines and recommendations on what components should be included in MBI studies to improve data collection, allow for rigor, replicability, and cross-study comparison, and advance biomedical research. Other health research fields, such as physical therapy, have benefited from similar standards for intervention studies.

Development of the NIH Music-Based Intervention Toolkit: Methodology and Approach

To develop the NIH MBI Toolkit, a planning committee of 12 NIH staff, including program directors experienced in scientific program development and a subset of the larger Trans-NIH Music and Health Working Group, was established. Through a collaborative process and with input from the NIH Director and the Trans-NIH Music and Health Working Group, the committee met regularly to reach consensus on the workshop format, the selection of external expert panel members, and the development of the workshop agenda.

To thoroughly assess the state of the music and health research field, a broad and comprehensive review of literature and databases of randomized controlled MBI studies was conducted using sources such as PubMed, Embase, Web of Science, PsycINFO, ClinicalTrials.gov, and the International Clinical Trials Registry Platform. The findings from these searches were supplemented by including publications cited by workshop content experts and other NIH investigators in relevant fields like neuroscience, music therapy and music medicine, behavioral intervention development, clinical trial methodology, and patient arts and advocacy.

To achieve the goal of providing research guidelines and recommendations for MBI development, it was crucial to identify panelists with direct expertise and to assemble a truly interdisciplinary panel. For each workshop, the panel included five or six experts representing disciplines such as neuroscience, music therapy and music medicine, behavioral intervention development, clinical trial methodology, and patient and arts advocacy. These experts were selected based on their published work, participation in other scientific meetings, and recommendations from NIH staff and external stakeholders. The NIH planning committee was particularly careful to include both scientists actively working in music and health research and experts in relevant disciplines not directly involved in MBI research, to bring fresh and unbiased perspectives to the panel.

Before each workshop, panelists worked closely with the NIH planning committee and participated in two or three premeetings for in-depth discussions of relevant questions provided in advance. Each workshop was organized around a specific theme, beginning with a keynote address and followed by a moderated discussion. This approach was adopted to encourage rich discussions, diverse opinions from the interdisciplinary panelists, and comments from a general audience comprising basic and clinical scientists, music professionals, staff from the NIH and other federal agencies, and the public. A unique aspect of this process was the presentation of demonstration projects by two interdisciplinary subgroups formed from the pool of panelists. These subgroups were tasked with developing an MBI for a specific disease or condition, applying the guiding principles established throughout the three-workshop series. To further inform the Toolkit's development, additional feedback was gathered from the external community through a formal request for information. It is important to note that the primary goal of the NIH MBI Toolkit is to provide standards and tools for investigators seeking NIH funding for their MBI studies, and as such, many of the selected panelists were based in the United States. The limited representation from international researchers engaged in music and health research may be a potential limitation of the development process.

Essential Components of the NIH Music-Based Intervention Toolkit

The NIH MBI Toolkit was designed to encourage the inclusion of the following elements in music-based intervention studies: a conceptual model or framework to guide the intervention's design, a clear research question supported by data to test the proposed hypothesis, a core set of common data elements that must be included in every MBI, a comprehensive description of the intervention, a detailed protocol for delivering the intervention, identification of the population to be studied, and appropriate control groups or comparators.

Conceptual Models for Music-Based Interventions

Developing a conceptual framework is a crucial first step for testing a music-based intervention (MBI). This framework represents the expected relationships between variables, similar to a hypothesis. The choice of a framework depends on the target population, specific intervention, comparison group, outcome measures, research question, and the study's stage and design.

Several models can guide MBI research. The experimental medicine framework, exemplified by the NIH Science of Behavior Change (SOBC) program, focuses on identifying and testing how interventions change behavior. For MBIs, this model would highlight how music components influence cognitive, emotional, or physiological processes to achieve desired outcomes.

The music therapy framework considers various factors, including the clinical setting, client population, age, diagnosis, and the therapist's approach (e.g., psychodynamic, humanistic, behavioral). Music therapists combine these approaches to address client needs, for instance, using a humanistic approach to build trust while integrating a behavioral approach to guide attention towards a desired response. It is important to ensure the chosen approach meets all client needs.

A neuromechanistic framework requires studies to connect clinical and basic research by providing a clear understanding of the underlying brain mechanisms. Given that music engages many neural systems—such as perception, sensory-motor, memory, attention, and emotion/reward systems—MBIs should align the intervention with the neural subsystems affected by a disorder. For example, music's ability to activate the brain's reward system could be useful for conditions involving motivational problems.

The resilience framework uses a contextual support model of music therapy based on motivational coping theory. This framework suggests that therapeutic music environments provide elements that support a person's independence, allow free expression, and encourage interaction with their surroundings. Music engagement then helps reduce stress or lessen risky situations.

Research Questions and Supporting Data

Music and health research should build on existing evidence from prior studies, literature, and clinical practice. Researchers can learn from the development of behavioral interventions, which emphasizes systematic reviews and using basic research to inform theoretical models. The initial literature review for MBIs should also focus on the specific components of the intervention, such as musical elements (e.g., frequency, tempo, melody, playback volume) and nonmusical elements (e.g., imagery), which are important for engaging proposed mechanisms or achieving desired outcomes.

Music-Based Intervention Components

The essential components of a music-based intervention (MBI) include the intervention itself, how it is delivered, the target population, a study design that differentiates MBI effects from general social interaction during the experience, and how data are collected and managed.

When defining the intervention, investigators should consider specific musical elements (e.g., pitch, timbre, rhythm), modes of engagement (e.g., actively performing, creating, or passively listening), the link between cultural music preferences and outcomes, patient demographics, and contextual factors. In early research stages, dose and frequency should be treated as variables to be adjusted for maximum effectiveness. Longer, lower-intensity interventions or booster sessions may be needed to maintain treatment benefits.

Protocol delivery methods are critical, as many studies have not consistently checked whether interventions were implemented uniformly. Key considerations for MBI delivery include: music characteristics (live vs. recorded, genre, personalized vs. non-personalized), research setting (lab, clinic, community), participation mode (individual vs. group, in-person vs. remote), timescale (session length, frequency, follow-up), staffing and support (expert therapists vs. trained providers), resources (cost, scheduling, assessments), and overall feasibility and patient adherence. These factors help determine the intervention's intensity needed for clinical improvements and its broader adoption.

The study population is the specific group of individuals chosen for a study from the broader target population (e.g., early-onset dementia patients from nursing homes). Researchers must have a strong justification for testing an intervention in a particular population, considering factors like disorder type, severity, convenience for pilot studies, control group availability, or the importance of longitudinal tracking. Practical considerations include population accessibility, stability, and available resources. Screening for confounding variables, such as hearing loss, is also recommended.

Control groups or comparators are essential for randomized controlled trials, though not always needed in early research phases. The study's goals and research question should guide the selection of the comparator. Designing appropriate control conditions for music studies is complex, as current research often has poorly designed ones. Control conditions for MBIs may include a music element (e.g., a slowed version of the music, meaningless sounds, nature sounds) or a non-music element (e.g., an audiobook). The research team must decide if the control aims to manage effects unrelated to music (e.g., attention, sound stimulation, shared experience) or effects of the specific delivery method. Controls must also match the test intervention in engagement intensity to account for the Hawthorne effect and mimic placebo effects. An NIH expert panel has provided a useful framework for selecting and justifying control groups in behavioral trials.

Outcome Measures and Biomarkers for Brain Disorders

Music-based interventions (MBIs) for brain disorders of aging, such as Alzheimer's, Parkinson's, and stroke, offer compelling evidence for music's health benefits and serve as a model for future research. The NIH MBI Toolkit, developed with expert input, suggests core outcome measures and biomarkers for these conditions that researchers can use. Guiding principles and factors for selecting these measures are important.

When developing hypotheses on MBI impact, essential steps include choosing a conceptual framework for outcomes, selecting an appropriate study design, identifying relevant short-term and long-term outcomes, understanding moderating factors, and determining relevant biomarkers. Various measurement methods are available for each domain, including self-reports, performance-based tasks, direct observation, sensor technology, physiological monitoring, and brain imaging. Each method has strengths and weaknesses, so using multiple assessment types for a given domain is often preferred.

Mechanistic outcomes provide insights into biological or behavioral processes, disease progression, or how an intervention works. Clinical outcomes are measurable changes in health, function, or quality of life resulting from a treatment. These can be observed in studies involving healthy individuals or those with specific conditions.

A biomarker is a measurable characteristic that indicates normal biological processes, disease presence, or a response to therapy. Biomarkers help determine not only if an intervention works but also how it works. They can signal risk, predict or measure conditions, assess safety, or aid in diagnosis, prognosis, or monitoring.

Brain disorders of aging affect multiple biological, behavioral, and cognitive systems. Thus, a wide range of biomarkers can be considered when designing MBIs for these conditions, including markers for inflammation, brain structure, neurological function, gene expression, emotion, sensory and motor activity, and stress (e.g., galvanic skin response, pupillometry, and cortisol levels).

Policy and Recommendations

Scientific advancement relies on two key concepts: rigorous design and execution of research, and the ability to reproduce findings. In 2016, the National Institutes of Health (NIH) introduced its Rigor and Reproducibility Policy to ensure unbiased and well-controlled experimental design, methodology, analysis, interpretation, and reporting of results. This policy also promotes scientific integrity, public accountability, and social responsibility.

Music-based interventions (MBIs) offer a valuable therapeutic opportunity because they are typically low-cost, have few side effects, can often be scaled within healthcare systems, and are generally well-received by patients. The creation and spread of the NIH MBI Toolkit addresses a critical need in music and health research for improved data collection and scientifically rigorous studies. This toolkit is a necessary step to accelerate the integration of MBIs into healthcare.

Rigorous MBI research requires a team-based approach, bringing together diverse experts. A successful investigative team should be interdisciplinary, with expertise in the target population or health condition, intervention development, outcome measures (e.g., functional, biological), neuroscience, and relevant biomarkers. The team should also include methodological experts, statisticians, competent clinicians to deliver the intervention, stakeholders (e.g., patients or caregivers), skilled study coordinators for recruitment and data management, and someone experienced in managing funded research. Using the guiding principles of the NIH MBI Toolkit is strongly recommended for NIH-funded MBI studies.

Future Directions for Music-Based Interventions

Music-based interventions (MBIs) have the potential to influence important patient outcomes across the lifespan, such as managing symptoms, slowing disease progression, aiding rehabilitation, and improving quality of life for many conditions. A crucial step toward integrating MBIs into healthcare systems is the widespread adoption and implementation of the NIH MBI Toolkit's guiding principles for large-scale, rigorous, and repeatable evidence-based research.

The development of the NIH MBI Toolkit has highlighted the usefulness of various technological advances in behavioral measurement that allow for more precise and robust data collection. These include methods like Item Response Theory and computer adaptive testing, which form the basis of systems like the Patient-Reported Outcomes Measurement Information System (PROMIS) and the NIH Toolbox. These systems offer over 100 standardized measures, including many cognitive assessments, that are relevant across different health conditions and developed with state-of-the-art psychometric methods. They can complement the NIH MBI Toolkit and are freely available, though digital test administration may incur fees.

Other important advances include Ecological Momentary Assessment (EMA), which involves frequent data sampling often through smartphones, and passive sensor technologies (e.g., wearable or home-based sensors). EMA can also leverage neuroscience tools that provide detailed insights into brain circuitry and its changes over time.

Additionally, the Biomarkers, EndpointS, and other Tools Resource (BEST glossary) from the US Food and Drug Administration and the NIH clarifies terminology for biomarkers and endpoints, fostering new biomarker development specifically for music and health research. For example, auditory biomarkers derived from EEG could help in screening study participants. Other valuable tools include validated measures of music engagement, flow, creativity, and joy, as well as digital measures of facial expressions and movement, and innovative personalized music delivery systems, which would enhance the real-world applicability of MBI protocols. The NIH anticipates that the MBI Toolkit will be updated in the future to remain relevant for various diseases and conditions. The research community is encouraged to use this Toolkit to improve the rigor and replicability of MBIs.

Summary

In the last ten years, research into how the arts affect health has grown. Treatments that do not involve medication, such as music, are being explored for brain disorders common in older adults, including stroke, Parkinson's disease, and Alzheimer's disease. Studies suggest that music activates many brain areas, potentially strengthening brain networks involved in senses, movement, emotions, and memory. Since these areas are often affected by aging brain disorders, music could be a more affordable, less invasive, and more accessible treatment option than traditional drug therapies.

Over the past decade, significant progress has been made in understanding and developing music-based interventions (MBIs). For example, Rhythmic Auditory Stimulation (RAS), a type of neurologic music therapy, uses rhythmic sounds to help individuals with Parkinson's disease improve their walking, potentially reducing falls. This method also shows promise for other conditions affecting walking and balance, like stroke. Additionally, singing can strengthen breathing and swallowing muscles. Neurologic music therapy and melodic intonation therapy have been successful in helping patients with speech difficulties by improving their cognitive and emotional functions, and their ability to express language. Research has also investigated if MBIs can boost brain function in healthy older adults and those with Alzheimer's, and help manage common behavioral symptoms like anxiety or depression.

Despite the promise of MBIs for managing symptoms of aging brain disorders, a clear understanding of music's full therapeutic potential requires more large-scale, thorough, and well-designed studies. Recent reviews have noted that while MBIs offer benefits for individuals with dementia and brain injuries, high-quality studies are still needed before these approaches can be widely recommended for clinical use. Inconsistencies in how studies are designed and measured have made it difficult to draw specific conclusions about their treatment benefits. A main barrier to wider use of MBIs is the limited amount of data from strong studies. Many reports of positive effects come from personal stories or small studies. For instance, a 2020 report found insufficient evidence to conclude that music therapy consistently benefits agitation, anxiety, mood, or quality of life for people with dementia, highlighting that many studies lacked a scientific framework.

Another challenge for implementing MBIs broadly is the inconsistent way terms are used. Music-based interventions are generally split into two categories: music therapy and music medicine. Music therapy is a recognized health profession where music is used by qualified therapists to address a person's physical, emotional, thinking, and social needs within a therapeutic relationship. In contrast, music medicine involves patients listening to recorded or live music, often managed by a medical professional who is not a music therapist, treating music like a drug. Unlike music therapy, music medicine does not require a deep patient-therapist relationship. Clearly defining these two types of MBIs is crucial for evaluating their effects and outcomes. To help MBIs reach their full potential, they need to become more consistent and repeatable, aligning with policies that ensure research quality. This requires developing clear standards and tools for intervention studies. The National Institutes of Health (NIH), with partners, held workshops to gather insights, which led to the creation of the NIH MBI Toolkit. This toolkit provides guidelines and recommendations for what should be included in MBI studies to improve data collection, allow for stronger comparisons across studies, and advance research. Other health fields have benefited from similar standard-setting efforts.

Development of the NIH MBI Toolkit: Methodology and Approach

A planning committee made up of NIH staff was formed to prepare for the NIH-FNIH workshop series. This committee, which included program directors experienced in scientific development, met regularly to agree on the workshop format, select external expert panelists, and create the agenda.

To fully understand the current state of music and health research, a broad review of existing studies on music-based interventions (MBIs) was conducted using various research databases. The review also included publications recommended by workshop experts and other NIH researchers in fields like neuroscience, music therapy, clinical trial methods, and patient advocacy.

For the workshops, experts were chosen for their knowledge in key areas, forming an interdisciplinary panel. Each workshop's panel included five or six experts from fields such as neuroscience, music therapy, behavioral intervention development, clinical trial methods, and patient advocacy. Experts were selected based on their published work, participation in scientific meetings, and recommendations from NIH staff and other sources. The committee ensured the panel included both active music and health researchers and experts from related fields not directly involved in MBI research, to bring fresh and unbiased viewpoints.

Before each workshop, panelists met several times with the NIH planning committee for in-depth discussions. Each workshop focused on a specific theme, featuring a main presentation followed by a moderated discussion. This format encouraged detailed conversations and a wide range of opinions from panelists and the public, including scientists, music professionals, and government staff.

A unique part of this process involved two smaller interdisciplinary groups of panelists presenting demonstration projects. These groups were tasked with developing an MBI for a specific condition, applying the guidelines created during the workshop series. The external community also provided feedback to further inform the toolkit's development through a formal request for information. It is worth noting that a potential limitation of this development process is that most selected panelists were based in the United States, meaning there was less representation from international researchers.

Essential Components of the NIH MBI Toolkit

The NIH MBI Toolkit was developed to encourage the inclusion of several key elements in music-based intervention (MBI) studies. These elements include: a guiding conceptual model or framework for the intervention's design; a clear research question supported by data to test the proposed idea; a core set of common data points or basic components for every MBI; a full description of the intervention; a detailed plan for how the intervention will be delivered; a definition of the group of people to be studied; and a plan for control groups or comparisons.

Conceptual Models or Frameworks for MBIs

A key first step in studying a music-based intervention (MBI) is developing a conceptual framework. This framework outlines the expected relationships between different study variables, essentially forming the study's hypothesis. The choice of framework depends on factors like the target population, the specific intervention, the comparison group, and the desired outcomes, similar to models used in evidence-based medicine.

Several types of frameworks can guide MBI research. An experimental medicine approach, such as the Science of Behavior Change (SOBC) program, focuses on understanding the exact processes or mechanisms by which an intervention leads to changes. For example, it would identify which aspects of music influence specific cognitive or emotional changes. Music therapy frameworks, on the other hand, are influenced by clinical settings and patient needs, often combining different approaches like humanistic or behavioral methods to support patients. A neuromechanistic framework connects clinical and basic research by considering how music affects specific brain systems—like those for perception, memory, or emotion—and how these systems relate to the disorder being studied. For instance, music's ability to activate the brain's reward system could be used for conditions involving lack of motivation. Lastly, a resilience framework suggests that therapeutic music environments can help patients cope with stress by supporting their independence and encouraging interaction.

Research Question and Supporting Data for MBI Hypothesis Testing

Research on music-based interventions (MBIs) should always be based on existing evidence from previous studies, published literature, or clinical practice. Researchers in music and health can learn from those who develop behavioral interventions, emphasizing the need for thorough reviews of existing research. These reviews should help guide the selection of theoretical models while ensuring ethical considerations are met. Additionally, the initial literature review for MBIs should specifically examine the intervention's components, including musical elements like frequency, tempo, and melody, as well as non-musical elements like accompanying imagery. These details are important for engaging the intended mechanisms or achieving desired results.

MBI Building Blocks and Common Data Elements

Essential parts of a music-based intervention (MBI) study include the intervention itself, how it is delivered, the target population, the study design (which helps separate the MBI's effects from general social interaction), and how data are collected and managed.

Intervention

When defining an MBI, researchers should consider specific musical elements like pitch or rhythm, how participants engage (e.g., actively performing or passively listening), the role of cultural preferences, participant demographics, and contextual factors. Early in research, factors like the intervention's dose and frequency should be treated as experimental variables to determine the most effective approach. Longer interventions or booster sessions might be necessary to maintain treatment benefits.

Protocol Delivery

Many studies on music interventions have not adequately checked whether the treatment was consistently applied across different therapists or study sites. Important considerations for MBI delivery methods include the type of music (live or recorded, personalized or not), the research setting (e.g., clinic or community), participation mode (individual or group, in-person or remote), timeframe (short- or long-term sessions, follow-up frequency), staffing (expert therapists vs. other trained providers), resources (cost, scheduling), and overall feasibility and adherence. These factors are crucial for understanding the necessary intensity of the intervention to see clinical improvements and for making the intervention widely available.

Study Population

The study population refers to the specific group of individuals available for a study, such as people with early-stage dementia living in a nursing home. Researchers must have a strong reason for choosing a particular population for their MBI study, perhaps focusing on a specific subtype of a disorder, or selecting a group that is easy to access for a pilot study. Other practical considerations include how accessible the population is, its stability over time, and available resources in the setting where the intervention will be tested. It is also advisable to screen participants for factors that could skew results, such as hearing loss.

Control Groups or Comparators

While not always necessary for early research, control groups are vital for randomized controlled trials. The choice of a comparison group should be driven by the study's goals and research question. Designing appropriate control conditions for music studies can be complex, and current MBI research often uses poorly designed controls. For MBIs, control conditions might involve a modified version of the music, meaningless sounds, nature sounds, or even audiobooks, instead of music. The research team needs to determine if the control is designed to account for non-music-related effects (like attention or general sound stimulation) or for the specific method of intervention delivery. Control groups should also match the test intervention in terms of engagement and observation intensity to account for any effects simply from being studied. The ideal comparator provides the clearest answer to the main research question. An NIH expert panel has provided a helpful framework for selecting and justifying control groups in behavioral trials.

Potential Outcome Measures and Biomarkers: Examples for Brain Disorders of Aging MBIs

Music-based interventions (MBIs) for brain disorders common in older adults, such as Alzheimer's disease and stroke, offer strong evidence of music's health benefits and serve as a model for future research. The NIH MBI Toolkit, developed through discussions with expert panels, suggests core sets of outcome measures and biomarkers for these conditions that researchers can use in their studies.

Mechanistic and Clinical Outcome Measures

When developing ideas about how MBIs affect aging brain disorders, it is important to first choose a conceptual framework for the outcomes being measured. This involves selecting an appropriate study design, identifying short-term and long-term outcomes, and determining relevant biomarkers. For each area of interest, various measurement methods are available, including self-reports, performance-based tasks, direct observation, sensor technology, and brain activity measurements. Each method has its own strengths and weaknesses, so using multiple methods to assess a single area is often best. Mechanistic outcomes provide insights into biological or behavioral processes, the nature of a disease, or how an intervention works. Clinical outcomes, in contrast, are measurable changes in health, function, or quality of life that result from a treatment or intervention, observed from an objective starting point.

Biomarkers

A biomarker is a specific characteristic that can be objectively measured to indicate normal biological processes, a disease process, or the body's response to a treatment. Biomarkers can help identify which parts of an intervention are truly causing an effect, showing not just if an intervention works, but how it works. They can also indicate risk, predict or measure a condition, assess safety, or be used for diagnosis, prognosis, or monitoring. Since aging brain disorders are complex and affect many systems, a wide range of biomarkers can be considered for MBI studies. These include markers of inflammation, brain structure, neurological function, gene activity, emotional state, sensory and motor activity, and stress indicators like cortisol levels.

Policy Implications and Recommendations

Scientific progress relies on two key ideas: designing and conducting research with strict accuracy, and ensuring that findings can be repeated by others. In 2016, the NIH introduced its Rigor and Reproducibility Policy to ensure that researchers use fair and well-controlled methods for designing studies, collecting and analyzing data, and reporting results. This policy also promotes scientific honesty, public responsibility, and social accountability.

Music-based interventions (MBIs) offer a valuable treatment opportunity because they are often low-cost, have few side effects, can be easily scaled within healthcare systems, and are generally well-received by patients. The creation and sharing of the NIH MBI Toolkit address a critical need in the music and health field by providing guidelines for scientifically sound studies. This is a crucial step toward integrating MBIs into healthcare systems.

Conducting rigorous MBI research also requires a team approach, bringing together diverse groups with varied expertise and perspectives. For successful research, the team should be interdisciplinary, with knowledge of the specific patient population or health condition, intervention development, desired outcomes (like functional or biological changes), neuroscience, and relevant biomarkers. Such a team should include experts in methodology and statistics, qualified clinicians to deliver the intervention, stakeholders (like patients or caregivers), skilled coordinators to manage data collection and study adherence, and individuals experienced in managing funded research. The NIH strongly recommends using the guiding principles of the NIH MBI Toolkit for all NIH-funded MBI studies.

Final Points and Next Steps for MBIs

Music-based interventions (MBIs) have the potential to positively affect important patient outcomes, such as managing symptoms, slowing disease progression, aiding rehabilitation, and improving quality of life across many conditions throughout a person's life. A crucial step for integrating MBIs into healthcare systems is to widely share and apply the NIH MBI Toolkit's guidelines for rigorous, repeatable, and evidence-based research.

The creation of the NIH MBI Toolkit has highlighted how various technological advancements in measuring behavior can lead to more precise and reliable results. These advancements include Item Response Theory and computer adaptive testing, which are central to systems like the Patient-Reported Outcomes Measurement Information System (PROMIS) and the NIH Toolbox. These systems, which offer over a hundred measures, including many for cognition, are relevant across different health conditions and were developed using advanced psychometric methods. They can supplement the NIH MBI Toolkit and are generally available, though digital administration may involve technology fees.

Other technological progress includes Ecological Momentary Assessment (EMA), which involves frequent data collection over time, often via smartphones or wearable sensors, providing real-time information instead of relying on past recall. EMA can also use insights from neuroscience tools, which offer detailed and dynamic views of brain circuits and how they change.

Furthermore, a glossary of terms for Biomarkers, EndpointS, and other Tools (BEST), developed by the US Food and Drug Administration and the NIH, clarifies how biomarkers and study endpoints are used from basic research to clinical care. This resource creates opportunities for developing new biomarkers specifically for music and health research. For example, auditory biomarkers from brainwave recordings could help select participant groups for MBI studies. Other valuable tools include reliable measures of music engagement, creativity, and joy, as well as digital ways to measure facial expressions and movement. Innovative personalized music delivery systems could also make MBI protocols more reflective of real-world use. The NIH anticipates future research opportunities for updating the NIH MBI Toolkit to apply to various diseases and conditions across all age groups. Researchers are strongly encouraged to use this toolkit to enhance the quality and repeatability of MBIs.

Music and Brain Health

In the last ten years, there has been a significant increase in research on how the arts affect health and well-being. Non-drug approaches, like music, are being studied for treating brain disorders that come with aging, such as stroke, Parkinson's disease, and Alzheimer's disease. Evidence suggests that music uses many parts of the brain and may help strengthen brain connections involved in senses, movement, emotions, and memory. Because many of these areas are affected by brain disorders, music could be a cheaper, less invasive, and more accessible treatment option than traditional medications.

Significant progress has been made in understanding, developing, and proving the effectiveness of music-based interventions (MBIs) for various conditions. For example, rhythmic auditory stimulation (RAS), a type of neurologic music therapy using rhythmic sounds, has shown promise in treating walking problems in people with Parkinson's disease. RAS may reduce falls and episodes where movement suddenly freezes. Studies also suggest that RAS could help people with other brain conditions affecting walking and balance, like stroke or traumatic brain injury. Additionally, singing can improve breathing control and strengthen muscles used for swallowing and walking. Neurologic music therapy and melodic intonation therapy are successfully used to help patients with difficulty speaking by stimulating their brain functions and improving their ability to express language. Other research has explored whether MBIs can improve thinking skills in healthy older adults and those with Alzheimer's, and help manage behavioral symptoms like aggression or anxiety.

Despite the promise of MBIs for managing symptoms in aging-related brain disorders, large, well-designed, and thorough studies are needed to fully understand how music affects the brain and its potential as a treatment. Over the past five years, two major systematic reviews concluded that MBIs benefit people with dementia and acquired brain injury. However, these reviews also emphasized the need for high-quality trials before these interventions can be widely recommended for medical practice. A more recent review of MBIs for people with dementia living at home found that differences in study designs and measurements made it hard to draw specific conclusions about their potential benefits.

A main obstacle to widely using MBIs has been the lack of strong data from well-designed studies. Reports of beneficial effects often come from individual stories or small trials. For example, a 2020 report from the Agency for Healthcare Research and Quality (AHRQ) and the National Academies of Sciences, Engineering, and Medicine (NASEM) reviewed 35 studies on non-drug approaches, including music, for people with dementia. They concluded there wasn't enough evidence to say for sure if music therapy helped with agitation, anxiety, depression, mood, or quality of life for these individuals. Many of these studies also lacked a strong scientific foundation, making their results preliminary.

Another difficulty for widespread use of MBIs is the lack of consistent definitions. MBIs are generally divided into two main types: "music therapy" and "music medicine." Music therapy is a recognized health profession where music is used by a qualified therapist within a treatment relationship to address a person's physical, emotional, thinking, and social needs. In contrast, music medicine involves patients listening to recorded or live music, often managed by a medical professional other than a music therapist, with the music acting like a medication. Unlike music therapy, music medicine does not require a treatment relationship with the patient. A clear distinction between these two types of MBIs is important for evaluating how well treatments work. The National Institutes of Health (NIH) is very interested in using music's healing potential, with many of its institutes involved in the Trans-NIH Music and Health Working Group. For MBIs to reach their full potential, they must become more rigorous and repeatable, following the NIH's policy on research quality. This means developing clear standards and tools for intervention studies. The NIH, in partnership with other organizations, held workshops in 2021 to gather diverse ideas from experts and stakeholders. A direct result of these workshops is the NIH MBI Toolkit, which provides guidelines and recommendations for what elements should be included in an MBI study. This toolkit aims to improve data collection, allow for scientific rigor, repeatability, and comparison between studies, and advance biomedical research. Other health research fields, like physical therapy, have benefited from similar standards for their intervention studies.

Developing the NIH MBI Toolkit

To create the NIH MBI Toolkit, a planning committee of 12 NIH staff, experienced in scientific program development, was formed. Through a collaborative process, and with input from the NIH Director and a larger working group, the committee regularly met to agree on the workshop format, select external experts, and develop the workshop agenda.

To fully assess the current state of music and health research, the committee conducted a broad and thorough review of existing research, including randomized controlled trials on MBIs from various scientific databases. They also included publications suggested by workshop experts and other NIH researchers in relevant fields like neuroscience, music therapy, behavioral intervention development, and clinical trial methods.

To provide the research community with guidelines for MBI development, it was crucial to identify panelists with specific expertise and to create a truly interdisciplinary group. For each workshop, the panel included five or six experts from fields such as neuroscience, music therapy, clinical trial methods, and patient advocacy. These experts were chosen based on their published work, participation in scientific meetings, and recommendations from NIH staff and outside groups. The NIH planning committee made sure the expert panel included both scientists actively working in music and health and experts from related fields who were not directly involved in MBI research, to bring fresh and unbiased perspectives.

Before the workshops, panelists prepared through detailed discussions. Each workshop featured expert talks and moderated discussions to encourage a wide range of ideas, and public feedback was also gathered. A unique part of this process was the presentation of sample projects by two interdisciplinary subgroups of panelists. These groups were tasked with developing an MBI for a specific disease or condition, applying the guiding principles created during the three-workshop series. A potential limitation in developing the Toolkit was that most experts involved were from the United States, which meant fewer international research perspectives were included.

Essential Components of the NIH MBI Toolkit

The NIH MBI Toolkit was designed to encourage the inclusion of the following elements in MBI studies: (1) a clear conceptual model or framework to guide the intervention's design, (2) a specific research question supported by data to test the proposed idea, (3) a core set of common data elements that must be included in every MBI study, (4) a full description of the intervention, (5) a detailed plan for delivering the intervention, (6) the specific group of people to be studied, and (7) control groups or comparison methods.

Conceptual Models or Frameworks for MBIs

A first step in testing an MBI is creating a conceptual framework for the proposed intervention, which shows the expected relationship between different factors. Similar to the PICOS model (population, intervention, comparison, outcome, and study design) used in evidence-based medicine, the choice of a framework depends on the target group, the specific intervention, the comparison group, the outcome measures, and the research question, as well as the study's stage and design. Below are four examples of models or frameworks that can be used in MBI research: experimental medicine, music therapy, neural, and resilience/social science.

Experimental Medicine Framework

The NIH Science of Behavior Change (SOBC) program demonstrates an experimental medicine approach. It identifies and tests the proposed ways therapies work at each stage of intervention development, aiming to better understand how human behavior changes. In SOBC, the focus is on defining the processes or mechanisms that cause behavior change or an intervention's effect. It also verifies the intended mechanism of action and identifies ways to measure whether that mechanism is engaged. For example, in an MBI, this model would highlight which parts of music are likely to influence the processes (like thinking, emotions, or physical responses) that lead to changes in the desired outcome, such as improved thinking skills or emotional well-being.

Music Therapy Framework

Many factors influence the guiding framework for music therapists, including the clinical setting, the type of client, their age, diagnosis, and the therapist's own theoretical approach. Music therapists combine different types of frameworks to meet their clients' needs, such as approaches that focus on psychology, human nature, behavior, or music itself. For instance, a therapist might use a humanistic approach to build respect and trust with clients, then add a behavioral approach that uses music as a cue to redirect attention and achieve a desired response. When choosing which approaches are best, it's important to make sure all clients' needs will be addressed by the selected combination.

Neuromechanistic Framework

The neuromechanistic framework requires that intervention studies provide a clear scientific basis that links clinical and basic research. Since music is known to engage many brain systems, including those for perception, movement, memory, attention, and emotion/reward, MBIs for a given disorder should consider how the brain systems involved in both the disorder and the intervention align. For example, music's ability to activate the brain's reward system could be used for conditions involving lack of motivation or inability to feel pleasure.

Resilience Framework

The resilience framework uses a model of music therapy based on a theory about how people cope. This framework suggests that therapeutic music environments have elements that support independence, encourage free expression, and promote patients' interaction with their surroundings. Engaging with music is then used to reduce stressful situations or lessen the impact of risky conditions.

Research Question and Supporting Data for MBI Hypothesis Testing

Research on music-based interventions should be built upon evidence from previous studies, existing literature, or clinical practice. Researchers in music and health can learn from those who develop behavioral interventions, which emphasizes the importance of systematic reviews and using basic research to choose theoretical models, while keeping an open mind about which treatment is best. Furthermore, the initial review of existing research should also focus on the specific parts of the intervention itself. For MBIs, this includes musical elements like pitch, rhythm, and volume, as well as non-musical parts (such as imagery that goes with the music), which are important for engaging the proposed mechanisms or producing the desired results.

MBI Building Blocks and Common Data Elements

The key parts of a music-based intervention (MBI) include the intervention itself, how it is delivered, the target population, a study design that separates the MBI's effects from general social interaction, and methods for data collection and management.

Intervention

When defining an intervention, researchers should consider specific musical elements (like pitch, sound quality, or rhythm), ways of participating (such as actively performing or creating music, or simply listening), how cultural music preferences relate to outcomes, patient background, and surrounding factors. In the earliest stages of research, elements like the amount and frequency of the intervention should be treated as experimental variables that can be adjusted to improve effectiveness. Also, longer, lower-intensity interventions, or adding "booster" sessions, may be important to maintain the desired treatment effect. The "building blocks" of an MBI in a research setting, including the reason for the study, the patient group, and the study's goal, will be determined by the research question.

Protocol Delivery

A systematic review of music intervention trials found that less than half of them checked how consistently the treatment was given across different practitioners and study sites. Similar issues were noted in a 2020 report. Important considerations for how MBI studies are delivered include: (1) music characteristics (e.g., live vs. recorded, music style, personalized vs. general, and patient-chosen vs. investigator-chosen); (2) research setting (e.g., laboratory, clinic, or community); (3) how participants engage (e.g., individual vs. group, in-person vs. remote); (4) time frame (e.g., short vs. long term, session length and intensity, follow-up frequency, or potential for getting used to the intervention); (5) staff and support (e.g., expert therapists vs. a range of trained providers); (6) resources and organization (e.g., cost, scheduling, or assessments); and (7) how practical, accessible, and well-followed the intervention is. These points are equally important for clearly determining the intensity of the intervention needed to see clinical improvements and to help spread and apply the intervention more widely.

Study Population

The study population is the specific group of people chosen for a study from a larger target group (for example, individuals diagnosed with early-onset dementia from a group of nursing home residents). Researchers conducting MBI studies must have a strong reason for testing a particular intervention in any given population. For instance, the choice of the target population might focus on specific types or severities of a disorder, the ability to easily find participants for a pilot study, the availability of a control group in a randomized trial, or the importance of studying how changes occur over time. Practical considerations also include how accessible the population is, how stable it is over time, and the resources available where the intervention will be tested. Additionally, it is wise to screen patients to rule out other factors that could affect results, such as hearing loss or an inability to recognize music.

Control Groups or Comparators

While control groups or comparison groups may not always be needed for early research, they are crucial for randomized controlled trials. The goals of a study and the research question should guide the selection of the comparison group. Designing the right control condition for music studies is complex, and current MBI research often uses poorly designed control conditions. For MBI studies, control conditions might include a musical element, such as a slowed-down version of the same music, meaningless sounds, or nature sounds. Alternatively, the control might involve something non-musical, like an audiobook. The research team needs to consider whether the chosen control condition is meant to account for intervention effects that are not related to the music itself (e.g., attention, sound stimulation, visual stimulation, and shared experience) or for effects of the specific way the intervention protocol is delivered (e.g., listening to recorded music versus guided or tailored music listening). Furthermore, control conditions must match the test intervention in how much engagement and observation they involve, to control for the "Hawthorne effect" (where people change behavior because they know they're being watched). Equally important is controlling for a possible placebo effect by matching the intensity of the treatment delivered in the test intervention. The best comparison group is one that will provide the clearest answer to the main research question or the strongest test of the trial's main idea. The reason for choosing the comparison should focus on the trial's main purpose and not be weakened by less important considerations or arbitrary rules. An NIH expert panel has provided a helpful framework for considering and justifying control groups using a "Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials."

Potential Outcome Measures and Biomarkers: Examples for Brain Disorders of Aging MBIs

Music-based interventions (MBIs) for aging-related brain disorders, including Alzheimer's, Parkinson's, and stroke, offer some of the most convincing evidence for music's health benefits and set an example for future work across all ages. The NIH MBI Toolkit, developed through discussions with expert panels, suggests core sets of outcome measures and biological markers (biomarkers) that researchers can choose for their MBI studies. Certain guiding principles and factors should be considered when selecting these outcome measures and biomarkers.

Mechanistic and Clinical Outcome Measures

The key steps in developing ideas about the impact of MBIs for aging-related brain disorders include adopting a conceptual framework for the results to be measured; choosing the right study design; identifying relevant areas for short-term and long-term outcomes, conditions that limit the effects, and mechanisms (how the intervention works); and determining the relevant biomarkers. For each area, many ways of measuring are possible, including self-reports, performance-based tasks, direct observation, sensor technology, physical monitoring, and various brain function measures. Each of these has strengths and weaknesses for assessing the area of interest. Therefore, using multiple assessment methods for a given area is often better. Mechanistic and clinical outcomes can come from studies using an intervention in healthy people or those with a specific disease to better understand human biology or disease. More specifically, mechanistic outcomes provide insights into biological or behavioral processes, the cause of a disease, or how an intervention works. Clinical outcomes are measurable changes from a starting point in health, function, or quality of life that result from a treatment or intervention.

Biomarkers

A biomarker is a specific characteristic that can be objectively measured and used as an indicator of normal biological processes, a disease process, or how the body responds to a drug treatment. Biomarkers can help identify which components of an intervention are truly active and provide insights into not just whether an intervention works, but also how it works. Biomarkers can indicate a person's risk or susceptibility to a condition; predict or measure a condition or response; assess safety; or be used for diagnosis, prognosis, or monitoring. Aging-related brain disorders are complex conditions affecting many biological, behavioral, and cognitive-emotional systems. As such, a wide range of biomarkers can be considered when designing MBIs for these disorders, including those related to inflammation, brain structure, neurological function, gene activity, mood, sensory and motor activity, and stress markers (e.g., skin conductance, pupil size, and cortisol levels).

Policy Implications and Recommendations

Progress in science depends on two crucial ideas: careful design and execution of scientific research, and the ability to reproduce research findings. In 2016, the NIH announced its Rigor and Reproducibility Policy to better ensure that researchers use fair and well-controlled experimental design, methods, analysis, interpretation, and reporting of results. Furthermore, this policy promotes scientific integrity, public accountability, and social responsibility.

Music-based interventions are a valuable treatment opportunity because they are low cost, mostly have no side effects, can often be used widely in healthcare systems, and are generally well-accepted by patients. The development and sharing of the NIH MBI Toolkit addresses an urgent need in the music and health field for better data collection, with guidelines for scientifically rigorous studies. This is a necessary step to speed up progress towards including MBIs in healthcare systems.

Rigorous MBI research also requires a team approach, bringing together different groups with varied expertise, perspectives, and ideas. To be successful, the research team should include experts from various fields, such as the specific patient population or health condition of interest, intervention development, target outcomes (e.g., functional, biological, disease, and non-disease outcomes), neuroscience, and relevant biomarkers. The team should also include methodologists, statisticians, competent clinicians to deliver the intervention, stakeholders (like patients or caregivers), skilled study coordinators to oversee recruitment, data collection, adherence to the study plan, and data management, and an individual experienced in managing funded research. Using the guiding principles of the NIH MBI Toolkit is strongly recommended for MBI studies funded by the NIH.

Future Directions for Music-Based Interventions

Music-based interventions (MBIs) have the potential to influence important patient outcomes, such as managing symptoms, slowing disease progression, aiding rehabilitation, and improving quality of life across many conditions and throughout a person's life. A critical step toward including MBIs into healthcare systems in the United States is to widely share and apply the guiding principles of the NIH MBI Toolkit for large-scale, rigorous, and repeatable evidence-based research.

The Toolkit's creation highlighted the value of new technologies for better measurement. These include systems like PROMIS and the NIH Toolbox, which offer reliable ways to assess outcomes across different conditions and were developed using the latest methods. The NIH Toolbox, in particular, has over 100 separate measures, including many for thinking skills, and is very useful. Parts of the NIH Toolbox and PROMIS can complement the NIH MBI Toolkit and are freely available, although there are fees for using them digitally.

Other advanced tools also help improve research, such as Ecological Momentary Assessment (EMA), which collects detailed data over time, often through smartphones or text messages, rather than relying on people remembering past events. Passive sensor technologies (like smartphones, wearable devices, or home-based sensors) can also be used. EMA can also take advantage of advances in neuroscience tools, which provide very dynamic and detailed pictures of brain circuits and how they change over time.

Additionally, the Biomarkers, EndpointS, and other Tools Resource glossary, developed by the US Food and Drug Administration and the NIH, clarifies terms and uses for biomarkers and study endpoints, as they relate to progress from basic research to medical product development to patient care. This resource creates unique opportunities for developing new biomarkers specifically for the music and health field. For example, auditory biomarkers derived from brain wave recordings (EEG) could be used to screen or group participants in an MBI study. Other useful tools and measures include reliable and validated measures of music engagement, flow, creativity, and joy. Digital measures of facial expressions and movement, as well as innovative personalized music delivery systems, would further enhance how realistic and applicable MBI protocols are in real-world settings.

Finally, the NIH anticipates future research opportunities for modifying and updating the NIH MBI Toolkit to apply to various diseases and conditions throughout life. The research community is strongly encouraged to use this Toolkit to help improve the rigor and repeatability of music-based interventions.

Music and Health Research

Over the last ten years, more studies have looked at how music affects health. Music is being explored as a way to help people with brain problems that come with age, like stroke, Parkinson's disease, and Alzheimer's disease. Music seems to use many parts of the brain. It might help make brain connections stronger for things like how senses work, how the body moves, feelings, and memory. Since many brain problems affect these areas, music could be a simpler and easier way to help than regular medicine.

Much has been learned about music-based help for different health problems. For example, a music therapy called rhythmic auditory stimulation (RAS) helps people with Parkinson's disease walk better. RAS can lower how often they freeze or fall. Studies show RAS may also help people with other brain problems that affect walking, like stroke or brain injury. Singing can help with breathing and make muscles stronger for swallowing and walking. Other music therapies help people with speaking problems after a brain injury to think better, feel better, move better, and speak more clearly. Some studies have looked at if music can help healthy older adults think better, and if it can help with actions and feelings that come with Alzheimer's, such as anger or sadness.

Even though music shows promise for brain problems in older adults, more large, careful studies are needed. These studies would help fully understand how music affects the brain and what it can really do. A big problem is that there is not enough strong study data about music-based help. What we know about music's good effects often comes from personal stories or small studies. One review found there was not enough proof to say if music therapy helped with things like anger, worry, sadness, mood, or how well people lived. Also, many of these studies were not set up in a scientific way, so their results are just early findings.

Another issue is that people do not always use the same words for music-based help. Music-based help falls into two main types: music therapy and music medicine. Music therapy is a health job where trained therapists use music to help people with their body, feelings, thinking, and how they get along with others. Music medicine is when people listen to music, often recorded. This is usually managed by a medical helper, but not a music therapist. Here, the music acts like medicine. Music medicine does not need a special bond between the helper and the person. Knowing the difference between these two types of music-based help is important to see how well they work.

The NIH, a big health research group, is very interested in using music to help people. Many parts of the NIH are working together on this. For music-based help to really work its best, studies need to be done more carefully and in a way that can be copied. This means making clear rules and tools for how to do these studies. The NIH worked with other groups to hold three meetings. From these meetings came the NIH MBI Toolkit. This toolkit has rules and ideas for what to include in music studies. It helps make sure studies collect good information, can be repeated, can be compared, and help health research move forward.

Development of the NIH MBI Toolkit: Methodology and Approach

To create the NIH MBI Toolkit, a group of 12 NIH staff members was formed. They worked together to decide how workshops would be run, who the outside experts should be, and what topics would be discussed. To understand the current state of music and health research, they reviewed many studies from various health databases. They also looked at what experts in brain science, music therapy, and other fields had published.

It was important to find experts from different areas to help create the guidelines. Each workshop included 5 or 6 experts in fields like brain science, music therapy, study design, and patient support. These experts were chosen based on their work and ideas. The planning group made sure to include both scientists actively working with music and health, as well as experts who could offer new, unbiased ideas. Before each workshop, the experts met with the planning group to discuss questions. Each workshop focused on a specific theme and included a main talk, followed by a discussion. This helped create open talks and get many different ideas from experts and the public.

A special part of this process was when smaller groups of experts showed how they would design a music-based study for a certain health problem, following the new rules. This also helped improve the Toolkit. One thing to note is that most experts chosen were from the United States. This might mean the Toolkit does not fully include ideas from music and health researchers around the world.

Essential Components of the NIH MBI Toolkit

The NIH MBI Toolkit was made to ensure that music-based help studies include these key parts: a clear plan for the study, a clear research question with facts to support it, a set of common facts to collect, a full description of the music help, detailed steps for giving the help, a clear idea of the people being studied, and other groups to compare results with.

Conceptual Models or Frameworks for MBIs

When testing music-based help, a plan or "framework" is needed. This plan shows how different parts of the study might be connected. Choosing the right plan depends on who is being studied, what the music help is, what it is compared to, what results are being looked for, and the main question of the study. Below are four types of plans that can be used.

Experimental Medicine Framework

This plan focuses on finding out exactly how a treatment works to change behavior. In a music study, this plan would point to which parts of the music are likely to cause changes in how people think, feel, or act. It would also help to measure if the music is indeed working on those specific areas of the brain or body.

Music Therapy Framework

Many things guide music therapists, like where they work, who they help, and what their ideas are about therapy. Music therapists use different kinds of plans to help people, combining ideas about feelings, behavior, or just focusing on the music itself. For example, a therapist might use a plan that helps build trust with a person, then use music to help them focus their attention in a certain way. It is important to pick plans that will best help all the person's needs.

Neuromechanistic Framework

This plan looks at how music help connects to the brain. Since music uses many brain systems—like those for hearing, moving, memory, attention, and feelings—studies should think about how the music help lines up with the brain systems affected by a health problem. For example, if music makes people feel good, it could be used for problems where people lack motivation or joy.

Resilience Framework

This plan uses music therapy to help people cope with stress. It suggests that places where music therapy happens should help people feel free to express themselves and interact with their surroundings. Engaging with music is then used to lower stress or lessen tough situations.

Research Question and Supporting Data for MBI Hypothesis Testing

Studies on music and health should build on what has been learned before. Researchers can learn from how other behavioral studies are done, which means carefully reviewing past information and using basic science to choose the best plans. The first step should also include looking at the parts of the music itself, such as how fast it is, the tune, or how loud it is. It should also consider non-music parts, like pictures that go with the music, because these can be important for getting the desired results.

MBI Building Blocks and Common Data Elements

Important parts of a music study include the music help itself, how it is given, who it is for, how the study is set up to see if the music really helps (not just being around people), and how information is collected.

Intervention

When planning the music help, studies should think about the music itself (like sound or tune), if people will play or listen, if their culture matters, and who the patient is. At first, how much music is given and how often should be tested. Longer, gentler help, or added follow-up sessions, might be needed to keep the good effects going. The decisions about the music help will depend on the study's goal and who is being studied.

Protocol Delivery

How the music help is given is also very important. Studies need to make sure the help is given the same way every time. Things to think about include if the music is live or recorded, what kind of music it is, if it is picked by the person or study helper, where it happens (home or clinic), if it is for one person or a group, how long it lasts, and if there are enough trained helpers. These things help show how strong the music help needs to be to work well and spread to more people.

Study Population

The study group is the people who are part of the study. Researchers need a good reason for choosing a certain group for a music study. For example, they might pick people with a certain type of illness, or people who are easy to find for a small study. It is also wise to check if people have other issues, like hearing loss, that might affect the study.

Control Groups or Comparators

In studies, a "control group" is often needed to compare with the group getting the music help. This group helps show if the music is truly making a difference. Picking the right control group for music studies can be tricky. A control group might listen to slow music, no-meaning sounds, nature sounds, or audiobooks. Researchers must think about what the control group is trying to balance out. For example, is it just about hearing sound, or is it about being around other people? The control group should be as involved as the group getting the music help, so people do not get better just because they are getting attention. The goal is to pick a control group that best answers the main question of the study.

Potential Outcome Measures and Biomarkers: Examples for Brain Disorders of Aging MBIs

Music-based help for brain problems in older adults shows great promise and can be a guide for future work. The NIH MBI Toolkit offers ideas for what to measure in these studies. When choosing what to measure, researchers should consider several factors.

Mechanistic and Clinical Outcome Measures

To test how music help works, researchers first decide what results they want to measure. This includes how people feel, act, or how their brain works. They can use different ways to measure this, like asking people, watching them, or using special sensors. It is often best to use a few different ways to measure things. Measures can look at how the body or brain works (mechanistic outcomes) or at real-world changes in health or daily life (clinical outcomes).

Biomarkers

A "biomarker" is something that can be measured to show how the body or brain is doing, or how it reacts to help. Biomarkers can help explain how music help works, not just if it works. They can show risk for a problem, predict how someone will react to treatment, or help diagnose issues. For brain problems, many biomarkers can be looked at. This includes things like how the brain is structured, how it works, how a person feels, how their body reacts to stress, and even their genes.

Policy Implications and Recommendations

Science moves forward when studies are designed well and their findings can be repeated by others. In 2016, the NIH set rules to make sure studies are fair, well-controlled, and their results are clear. These rules also encourage good science, being open about how money is used, and helping society.

Music-based help is a valuable way to help people because it often costs less, has few side effects, can be used widely in healthcare, and most people like it. The NIH MBI Toolkit helps fill a big need in the music and health field by giving guidelines for strong scientific studies. This is a needed step to move closer to using music-based help in healthcare systems.

Strong music research also requires different groups working together, bringing their unique skills and ideas. A study team should include experts in the health problem being studied, in creating treatments, in brain science, and in measuring outcomes. The team should also have a method expert, a statistician, a skilled helper to give the music, people who are affected by the problem (like patients or their caregivers), and a coordinator to manage the study. Following the guidelines in the NIH MBI Toolkit is strongly suggested for any music-based study funded by the NIH.

Final Points and Next Steps for MBIs

Music-based help has the power to change important results for patients, such as managing symptoms, slowing down illnesses, helping with recovery, and making life better for many health problems across a person's life. A key step to bringing music-based help into healthcare is to share and use the NIH MBI Toolkit's guidelines for large, strong, and repeatable studies that are based on evidence.

The NIH MBI Toolkit was made using new ways to measure human behavior. These include methods that adapt to how people respond, like those used in systems called PROMIS and NIH Toolbox. These systems are useful for different health problems and were built using modern measurement science. The NIH Toolbox, with over 100 tools for measuring things like thinking skills, is very helpful. Parts of these tools can work with the NIH MBI Toolkit and are free to use, though there may be costs for digital testing.

Other new tools include "Ecological Momentary Assessment" (EMA), which collects information often using smartphones. Also, special sensors can be used in phones, on the body, or in the home. EMA can also use new brain science tools that show how the brain works in detail and how it changes over time.

Also, a guide from the US Food and Drug Administration and the NIH helps explain words and uses for biomarkers in health research. This creates chances to find new biomarkers specifically for music and health. For example, brainwave readings related to hearing could be used to sort people for a music study. Other useful tools and measures include ways to measure how much someone engages with music, their creativity, and their joy. Digital ways to measure face expressions and movement, plus new ways to give personalized music, would make music studies more real-world.

Finally, the NIH sees chances to update the NIH MBI Toolkit in the future for different health problems and age groups. Researchers are strongly encouraged to use this Toolkit to help make music-based studies stronger and more repeatable.