Retention of touchscreen skills is compromised in Parkinson's disease

Everyday technology use among people with Parkinson's disease

OBJECTIVES

To explore the relevance of and ability to use everyday technology (ET) among people with Parkinson's Disease (PD) and to explore associations between ET use and global cognition and motor ability.

MATERIALS AND METHODS

Cross-sectional data was collected from 34 people with PD using the Short Everyday Technology Use Questionnaire+ (S-ETUQ+), the Movement Disorder Society-Unified Parkinson's Disease Rating Scale and the Montreal Cognitive Assessment (MoCA).

RESULTS

Out of 41 ETs in the S-ETUQ+, the mean number perceived as relevant was 27.5 (min-max 19-35, SD 3.6). A good ability to use ET was reported where many ETs had a challenge measure below participants' ability to use them. A strong positive correlation between the ability to use ET and global cognition (MoCA) (r = .676, p = <0.01) was shown.

CONCLUSIONS

ET use has become integrated into everyday life and is important for participation. This study showed a high relevance of and good ability to use ET and a correlation between ET use and global cognition among people with mild-moderate PD. Evaluation and support to use ET in PD are important for maintaining independence and participation, especially among those with cognitive decline.

Classification of Parkinson's Disease in Patch-Based MRI of Substantia Nigra

Parkinson's disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson's Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.

Motor Memory Consolidation Deficits in Parkinson's Disease: A Systematic Review with Meta-Analysis

BACKGROUND

The ability to encode and consolidate motor memories is essential for persons with Parkinson's disease (PD), who usually experience a progressive loss of motor function. Deficits in memory encoding, usually expressed as poorer rates of skill improvement during motor practice, have been reported in these patients. Whether motor memory consolidation (i.e., motor skill retention) is also impaired is unknown.

OBJECTIVE

To determine whether motor memory consolidation is impaired in PD compared to neurologically intact individuals.

METHODS

We conducted a pre-registered systematic review (PROSPERO: CRD42020222433) following PRISMA guidelines that included 46 studies.

RESULTS

Meta-analyses revealed that persons with PD have deficits in retaining motor skills (SMD = -0.17; 95% CI = -0.32, -0.02; p = 0.0225). However, these deficits are task-specific, affecting sensory motor (SMD = -0.31; 95% CI -0.47, -0.15; p = 0.0002) and visuomotor adaptation (SMD = -1.55; 95% CI = -2.32, -0.79; p = 0.0001) tasks, but not sequential fine motor (SMD = 0.17; 95% CI = -0.05, 0.39; p = 0.1292) and gross motor tasks (SMD = 0.04; 95% CI = -0.25, 0.33; p = 0.7771). Importantly, deficits became non-significant when augmented feedback during practice was provided, and additional motor practice sessions reduced deficits in sensory motor tasks. Meta-regression analyses confirmed that deficits were independent of performance during encoding, as well as disease duration and severity.

CONCLUSION

Our results align with the neurodegenerative models of PD progression and motor learning frameworks and emphasize the importance of developing targeted interventions to enhance motor memory consolidation in PD.

Digital Therapeutics in Parkinson's Disease: Practical Applications and Future Potential

Digital therapeutics, treatments delivered remotely and enabled by modern technology, facilitate the provision of personalized, evidence-based, interdisciplinary interventions to manage the complexities associated with Parkinson’s disease. In the context of the COVID-19 pandemic, the need for digital therapeutics has arguably never been greater. However, despite new advances in technology and a heightened interest due to the pandemic, digital therapeutics remain underdeveloped and underutilized. In this paper, we briefly review practical applications and emerging advances in digital therapeutic platforms that target motor and non-motor signs and healthy lifestyle behaviors such as regular exercise, a healthful diet and optimal sleep hygiene habits. Future applications which could transform personalized self-management and patient care are presented. Opportunities, drawbacks and barriers to access are discussed.

Impaired Touchscreen Skills in Parkinson's Disease and Effects of Medication

BACKGROUND

Deficits in fine motor skills may impair device manipulation including touchscreens in people with Parkinson's disease (PD).

OBJECTIVES

To investigate the impact of PD and anti-parkinsonian medication on the ability to use touchscreens.

METHODS

Twelve PD patients (H&Y II-III), OFF and ON medication, and 12 healthy controls (HC) performed tapping, single and multi-direction sliding tasks on a touchscreen and a mobile phone task (MPT). Task performance was compared between patients (PD-OFF, PD-ON) and HC and between medication conditions.

RESULTS

Significant differences were found in touchscreen timing parameters, while accuracy was comparable between groups. PD-OFF needed more time than HC to perform single (P = 0.048) and multi-direction (P = 0.004) sliding tasks and to grab the dot before sliding (i.e., transition times) (P = 0.040; P = 0.004). For tapping, dopaminergic medication significantly increased performance times (P = 0.046) to comparable levels as those of HC. However, for the more complex multi-direction sliding, movement times remained slower in PD than HC irrespective of medication intake (P < 0.050 during ON and OFF). The transition times for the multi-direction sliding task was also higher in PD-ON than HC (P = 0.048). Touchscreen parameters significantly correlated with MPT performance, supporting the ecological validity of the touchscreen tool.

CONCLUSIONS

PD patients show motor problems when manipulating touchscreens, even when optimally medicated. This hinders using mobile technology in daily life and has implications for developing adequate E-health applications for this group. Future work needs to establish whether touchscreen training is effective in PD.