Music and Technology: The Curative Algorithm

A transitional care program in a technologically monitored in-hospital facility reduces the length of hospital stay and improves multidimensional frailty in older patients: a Randomized Clinical Trial

BACKGROUND

Longer length of hospital stay (LOS) negatively affects the organizational efficiency of public health systems and both clinical and functional aspects of older patients. Data on the effects of transitional care programs based on multicomponent interventions to reduce LOS of older patients are scarce and controversial.

AIMS

The PRO-HOME study aimed to assess the efficacy in reducing LOS of a transitional care program involving a multicomponent intervention inside a technologically monitored in-hospital discharge facility.

METHODS

This is a Randomized Clinical Trial on 60 patients (≥65 years), deemed stable and dischargeable from the Acute Geriatrics Unit, equally assigned to the Control Group (CG) or Intervention Group (IG). The latter underwent a multicomponent intervention including lifestyle educational program, cognitive and physical training. At baseline, multidimensional frailty according to the Multidimensional Prognostic Index (MPI), and Health-Related Quality of Life (HRQOL) were assessed in both groups, along with physical capacities for the IG. Enrolled subjects were evaluated after 6 months of follow-up to assess multidimensional frailty, HRQOL, and re-hospitalization, institutionalization, and death rates.

RESULTS

The IG showed a significant 2-day reduction in LOS (median days IG = 2 (2-3) vs. CG = 4 (3-6); p < 0.001) and an improvement in multidimensional frailty at 6 months compared to CG (median score IG = 0.25(0.25-0.36) vs. CG = 0.38(0.31-0.45); p = 0.040). No differences were found between the two groups in HRQOL, and re-hospitalization, institutionalization, and death rates.

DISCUSSION

Multidimensional frailty is a reversible condition that can be improved by reduced LOS.

CONCLUSIONS

The PRO-HOME transitional care program reduces LOS and multidimensional frailty in hospitalized older patients.

TRIAL REGISTRATION

ClinicalTrials.gov n. NCT06227923 (retrospectively registered on 29/01/2024).

Feasibility of therapeutic music listening in fibromyalgia: a randomised controlled pilot study

BACKGROUND

Fibromyalgia patients can benefit from music approaches as complementary treatments. In the literature, it was shown that these interventions managed pain conditions as well as reduced complaints, increased relaxation, and improved moods.

OBJECTIVE

This study aimed at evaluating music therapy, in the form of therapeutic music listening, specifically for patients with fibromyalgia, to treat chronic pain by reducing pain perception, increasing well-being, and improving quality of life.

METHODS

Twenty-four patients with fibromyalgia were recruited to take part in this feasibility pilot study that adopted a between-subject and within-subject design. Participants were randomised into three groups: (1) standard care, (2) standard care plus preferred music listening, (3) standard care plus Melomics-Health music listening, composed by an algorithm. Participants in experimental groups listened to 30 min of music at home, twice a day for a month. Patients' perceptions of changes following the listening, the intensity of pain and its interference in their lives, physical and mental well-being, and reported attitudes towards listening to music were evaluated respectively through the patients' global impression of change, the brief pain inventory, the Short Form Healthy Survey-12, and the cognitive behavioural assessment-outcome evaluation.

RESULTS

The study showed that music listening can significantly affect mental well-being compared to no music. Moreover, the effects in the Melomics-Health group are maintained at follow-up. No significant effect on pain perception was noted.

CONCLUSIONS

The study provides information supporting a possible role of music listening in improving well-being of patients with fibromyalgia.

Effect of Algorithmic Music Listening on Cardiac Autonomic Nervous System Activity: An Exploratory, Randomized Crossover Study

It is proven that music listening can have a therapeutic impact in many clinical fields. However, to assume a curative value, musical stimuli should have a therapeutic logic. This study aimed at assessing short-term effects of algorithmic music on cardiac autonomic nervous system activity. Twenty-two healthy subjects underwent a crossover study including random listening to relaxing and activating algorithmic music. Electrocardiogram (ECG) and non-invasive arterial blood pressure were continuously recorded and were later analyzed to measure Heart Rate (HR) mean, HR variability and baroreflex sensitivity (BRS). Statistical analysis was performed using a general linear model, testing for carryover, period and treatment effects. Relaxing tracks decreased HR and increased root mean square of successive squared differences of normal-to-normal (NN) intervals, proportion of interval differences of successive NN intervals greater than 50 ms, low-frequency (LF) and high-frequency (HF) power and BRS. Activating tracks caused almost no change or an opposite effect in the same variables. The difference between the effects of the two stimuli was statistically significant in all these variables. No difference was found in the standard deviation of normal-to-normal RR intervals, LF_power in normalized units and LF_power/HF_power variables. The study suggests that algorithmic relaxing music increases cardiac vagal modulation and tone. These results open interesting perspectives in various clinical areas.

Conventional and Algorithmic Music Listening before Radiotherapy Treatment: A Randomized Controlled Pilot Study

Music listening is a widespread approach in the field of music therapy. In this study, the effects of music listening on anxiety and stress in patients undergoing radiotherapy are investigated. Sixty patients with breast cancer who were candidates for postoperative curative radiotherapy were recruited and randomly assigned to three groups: Melomics-Health (MH) group (music listening algorithmically created, n = 20); individualized music listening (IML) group (playlist of preferred music, n = 20); no music group (n = 20). Music listening was administered for 15 min immediately before simulation and during the first five radiotherapy sessions. The State-Trait Anxiety Inventory (STAI) and the Psychological Distress Inventory (PDI) were administered before/after treatment. Cochran’s Q test and McNemar test for paired proportions were performed to evaluate if the proportion of subjects having an outcome score below the critical value by treatment and over time was different, and if there was a change in that proportion. The MH group improved in STAI and PDI. The IML group worsened in STAI at T1 and improved STAI-Trait at T2. The IML group worsened in PDI at T2. The No music group generally improved in STAI and PDI. Clinical and music listening-related implications are discussed defining possible research perspectives in this field.

Effects of Algorithmic Music on the Cardiovascular Neural Control

Music influences many physiological parameters, including some cardiovascular (CV) control indices. The complexity and heterogeneity of musical stimuli, the integrated response within the brain and the limited availability of quantitative methods for non-invasive assessment of the autonomic function are the main reasons for the scarcity of studies about the impact of music on CV control. This study aims to investigate the effects of listening to algorithmic music on the CV regulation of healthy subjects by means of the spectral analysis of heart period, approximated as the time distance between two consecutive R-wave peaks (RR), and systolic arterial pressure (SAP) variability. We studied 10 healthy volunteers (age 39 ± 6 years, 5 females) both while supine (REST) and during passive orthostatism (TILT). Activating and relaxing algorithmic music tracks were used to produce possible contrasting effects. At baseline, the group featured normal indices of CV sympathovagal modulation both at REST and during TILT. Compared to baseline, at REST, listening to both musical stimuli did not affect time and frequency domain markers of both SAP and RR, except for a significant increase in mean RR. A physiological TILT response was maintained while listening to both musical tracks in terms of time and frequency domain markers, compared to baseline, an increase in mean RR was again observed. In healthy subjects featuring a normal CV neural profile at baseline, algorithmic music reduced the heart rate, a potentially favorable effect. The innovative music approach of this study encourages further research, as in the presence of several diseases, such as ischemic heart disease, hypertension, and heart failure, a standardized musical stimulation could play a therapeutic role.

Algorithmic Music for Therapy: Effectiveness and Perspectives

This study assessed the short-term effects of conventional (i.e., human-composed) and algorithmic music on the relaxation level. It also investigated whether algorithmic compositions are perceived as music and are distinguishable from human-composed music. Three hundred twenty healthy volunteers were recruited and randomly allocated to two groups where they listened to either their preferred music or algorithmic music. Another 179 healthy subjects were allocated to four listening groups that respectively listened to: music composed and performed by a human, music composed by a human and performed by a machine; music composed by a machine and performed by a human, music composed and performed by a machine. In the first experiment, participants underwent one of the two music listening conditions—preferred or algorithmic music—in a comfortable state. In the second one, participants were asked to evaluate, through an online questionnaire, the musical excerpts they listened to. The Visual Analogue Scale was used to evaluate their relaxation levels before and after the music listening experience. Other outcomes were evaluated through the responses to the questionnaire. The relaxation level obtained with the music created by the algorithms is comparable to the one achieved with preferred music. Statistical analysis shows that the relaxation level is not affected by the composer, the performer, or the existence of musical training. On the other hand, the perceived effect is related to the performer. Finally, music composed by an algorithm and performed by a human is not distinguishable from that composed by a human.

More music, more health!

For several decades, music has been used more and more frequently and consciously as a mean of care to reduce or stabilize symptoms and/or complications arising therefrom. This has been the case with several diseases and conditions. Indeed, music also gives pleasure, promotes well-being, facilitates the expression and regulation of emotions and improves communication and relationships between individuals. The basis underlying the therapeutic potential of music are to be considered in relation to the extensive action that music itself exerts on the brain but also on vital signs and neurochemical systems. Music therapy interventions are based on active/receptive approaches (characterized by a relational or rehabilitative component) but also on music listening. Music-based interventions can be considered activities aimed at increasing the person's well-being. The objectives of making/listening to music are to improve the person's mood and motivation, promote socialization and stimulate sensory, motor and cognitive aspects. In particular, music listening effects concern structured symptoms and general well-being reducing anxiety and stress. New technologies, such as algorithmic music and machine learning techniques, can also help to develop therapeutic interventions with music and to bring art and science closer together, in the service of medicine, in clinical work and in research.

Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial

BACKGROUND

The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods.

METHODS

Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation.

RESULTS

A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency.

CONCLUSIONS

The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.

Daily music listening to reduce work-related stress: a randomized controlled pilot trial

BACKGROUND

Literature shows that music can reduce stress conditions. This pilot study investigated the effects of music listening on work-related stress and well-being in healthcare professionals.

METHOD

A total of 45 subjects were randomly assigned to three treatment groups: No Music, Individualized Music and Melomics-Health Listening. Music groups experienced a daily 30-min-playlist listening for 3 weeks at home. The Maugeri Stress Index-Revised (MASI-R) and the Psychological General Well-Being Index (PGWBI) were administered at baseline, after 3 weeks and after 7 weeks (follow-up). Longitudinal data were analyzed by means of a nested ANOVA model, testing the main effects of time and treatment and the interaction between them.

RESULTS

MASI-R scores showed a positive trend in music groups and a worsening in the control group. Only the interaction time/treatment emerged as supporting a trend toward statistical significance (P = 0.07). PGWBI showed a stability in music groups and a clear decline in controls, without significant effects.

CONCLUSIONS

Results from the study support the need for a larger clinical trial: it is suggested that daily music listening could be implemented to reduce work-related stress and that the effects may be related, not only to individual musical preferences and familiarity, but also to specific music structures and parameters.

Cognitive Intervention with Musical Stimuli Using Digital Devices on Mild Cognitive Impairment: A Pilot Study

The effect of music therapy on cognitive function has been widely reported; however, its clinical implications remain controversial. Performing therapeutic musical activities in groups using individualized instruments can help overcome the issues of engagement and compliance. We aimed to evaluate the effect of a cognitive intervention with musical stimuli using digital devices on mild cognitive impairment (MCI). In this prospective study, 24 patients with MCI (intervention group, 12; and control group, 12) were enrolled. We developed an electronic device with musical instruments and the Song-based Cognitive Stimulation Therapy protocol (SongCST). Patients in the intervention group underwent a 10-week cognitive intervention involving musical stimuli generated by our device. Effect of the intervention on cognitive function was evaluated by the Mini-Mental State Examination-Dementia Screening (MMSE-DS), Montreal Cognitive Assessment-Korean (MOCA-K), and Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB). In the intervention group, MMSE-DS and MOCA-K scores improved significantly after the 10-week intervention. The changes in MOCA-K and CDR-SB scores were significantly different between the intervention and control groups. Our study showed that music therapy with digital devices has a positive effect on the executive function and overall disease severity in patients with MCI. Our study can facilitate individualization of music therapy using digital devices in groups.