Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Han E

PROVIDER: S-EPMC7151070 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Han Eunyoung E Park Jinse J Kim Haeyu H Jo Geunyeol G Do Hwan-Kwon HK Lee Byung In BI

Healthcare (Basel, Switzerland) 20200225 1

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 ...[more]

PMID: 32106559

Similar Datasets

Project description:BackgroundCommunity-based social participation has shown promise in delaying cognitive decline in older adults with mild cognitive impairment (MCI) who are at risk of developing dementia. Although group storytelling interventions have proven effective, the need for a skilled workforce to support people with MCI can limit broader community implementation. Technology-based interventions may offer a solution to this limitation by replicating the abilities of therapists.ObjectiveThis study aims to co-design a digital storytelling intervention and evaluate its usability.MethodsThis co-design process involved 3 stages, engaging people with MCI (n=12), their caregivers (n=4), and therapists (n=5) in Beijing, China. In the first stage, we used card sorting and voting methods to identify potential incentives for social participation and target the specific abilities that people with MCI wanted to enhance. In the second stage, we conducted brainstorming sessions with people with MCI and their caregivers to identify the potential features of a digital storytelling application named Huiyou ("meeting new friends" in Chinese). Finally, we assessed Huiyou's usability with people with MCI and therapists, leading to iterative improvements based on the usability findings.ResultsWe uncovered a crucial link between boosting the self-confidence of people with MCI and their ability to address social participation challenges. Notably, we identified memory improvement and enhanced language expression as key factors for effective communication with grandchildren. Subsequently, participants suggested features and interfaces to address these challenges, leading to the development of Huiyou, a group-based digital storytelling application featuring functions such as generating story materials, conducting memory retrieval activities, and sharing stories. It received an "excellent" rating in the User Experience Questionnaire benchmark, displaying high levels of attractiveness, dependability, stimulation, and novelty. People with MCI achieved an average task completion rate of 87% (n=19; SD 0.13) of the 22 tasks. However, feedback from people with MCI and therapists highlighted usability issues in navigation, activity management, user interface, and feature optimization, indicating a need for improved accessibility and efficiency.ConclusionsThe co-design approach contributed to developing the Huiyou prototype, supporting community-based social participation. User feedback highlighted the potential of Huiyou to enhance well-being and facilitate meaningful social interactions while maintaining crucial existing relationships.

Project description:Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer's Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman's ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman's ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman's ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer's disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.

Project description:BackgroundMild cognitive impairment (MCI) is a syndrome preceding more severe impairment characterized by dementia. MCI affects an estimated 15% to 20% of people older than 65 years. Nonpharmacological interventions including exercise are recommended as part of overall MCI management based on the positive effects of exercise on cognitive performance. Interval training involves brief intermittent bouts of exercise interspersed with short recovery periods. This type of exercise promotes cognitive improvement and can be performed in individuals with MCI. Synaptic plasticity can be assessed in vivo by the neurophysiological response to repetitive transcranial magnetic stimulation (rTMS). A method to assess synaptic plasticity uses an intermittent theta burst stimulation (iTBS), which is a patterned form of rTMS. Individuals with MCI have decreased responses to iTBS, reflecting reduced synaptic plasticity. It is unknown whether interval training causes changes in synaptic plasticity in individuals living with MCI.ObjectiveThis research will determine whether interval training performed using a cycle ergometer enhances synaptic plasticity in individuals with MCI. The three aims are to (1) quantify synaptic plasticity after interval training performed at a self-determined intensity in individuals with MCI; (2) determine whether changes in synaptic plasticity correlate with changes in serum brain-derived neurotrophic factor, osteocalcin, and cognition; and (3) assess participant compliance to the exercise schedule.Methods24 individuals diagnosed with MCI will be recruited for assignment to 1 of the 2 equally sized groups: exercise and no exercise. The exercise group will perform exercise 3 times per week for 4 weeks. Synaptic plasticity will be measured before and following the 4-week intervention. At these time points, synaptic plasticity will be measured as the response to single-pulse TMS, reflected as the percent change in the average amplitude of 20 motor-evoked potentials before and after an iTBS rTMS protocol, which is used to induce synaptic plasticity. In addition, individuals will complete a battery of cognitive assessments and provide a blood sample from the antecubital vein to determine serum brain-derived neurotrophic factor and osteocalcin.ResultsThe study began in September 2023.ConclusionsThe proposed research is the first to assess whether synaptic plasticity is enhanced after exercise training in individuals with MCI. If exercise does indeed modify synaptic plasticity, this will create a new avenue by which we can study and manipulate neural plasticity in these individuals.Trial registrationClinicalTrials.gov NCT05663918; https://clinicaltrials.gov/study/NCT05663918.International registered report identifier (irrid)PRR1-10.2196/50030.

Project description:ObjectiveTo compare the effects of a 4-week high-saturated fat/high-glycemic index (HIGH) diet with a low-saturated fat/low-glycemic index (LOW) diet on insulin and lipid metabolism, cerebrospinal fluid (CSF) markers of Alzheimer disease, and cognition for healthy adults and adults with amnestic mild cognitive impairment (aMCI).DesignRandomized controlled trial.SettingVeterans Affairs Medical Center clinical research unit.ParticipantsForty-nine older adults (20 healthy adults with a mean [SD] age of 69.3 [7.4] years and 29 adults with aMCI with a mean [SD] age of 67.6 [6.8] years).InterventionParticipants received the HIGH diet (fat, 45% [saturated fat, > 25%]; carbohydrates, 35%-40% [glycemic index, > 70]; and protein, 15%-20%) or the LOW diet (fat, 25%; [saturated fat, < 7%]; carbohydrates, 55%-60% [glycemic index, < 55]; and protein, 15%-20%) for 4 weeks. Cognitive tests, an oral glucose tolerance test, and lumbar puncture were conducted at baseline and during the fourth week of the diet.Main outcome measuresThe CSF concentrations of β-amyloid (Aβ42 and Aβ40), tau protein, insulin, F2-isoprostanes, and apolipoprotein E, plasma lipids and insulin, and measures of cognition.ResultsFor the aMCI group, the LOW diet increased CSF Aβ42 concentrations, contrary to the pathologic pattern of lowered CSF Aβ42 typically observed in Alzheimer disease. The LOW diet had the opposite effect for healthy adults, ie, decreasing CSF Aβ42, whereas the HIGH diet increased CSF Aβ42. The CSF apolipoprotein E concentration was increased by the LOW diet and decreased by the HIGH diet for both groups. For the aMCI group, the CSF insulin concentration increased with the LOW diet, but the HIGH diet lowered the CSF insulin concentration for healthy adults. The HIGH diet increased and the LOW diet decreased plasma lipids, insulin, and CSF F2-isoprostane concentrations. Delayed visual memory improved for both groups after completion of 4 weeks of the LOW diet.ConclusionOur results suggest that diet may be a powerful environmental factor that modulates Alzheimer disease risk through its effects on central nervous system concentrations of Aβ42, lipoproteins, oxidative stress, and insulin.

Project description:BackgroundContinuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI.MethodsWe used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation.ResultsThe final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61.ConclusionsOur findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets