Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments

Habets, Jeroen; Heijmans, Margot; Simons, Claudia; Leentjens, Albert F.G.; Temel, Yasin; Kuijf, Mark L.; Kubben, Pieter

doi:10.2196/15628

Cited by 18 publications

(41 citation statements)

References 33 publications

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“…Among 17 studies, 6 studies collected symptoms using a smartphone accelerometer and gyroscope [ 16 , 20 , 21 , 24 , 25 , 28 ]. Further, 7 studies used wearable devices [ 20 , 23 , 26 , 27 , 31 , 33 , 35 ], which included a smartwatch [ 20 , 33 ], a smart insole [ 20 ], an actigraph such as a Fitbit [ 23 ], and sensors attached to the ankle [ 31 , 35 ], chest [ 26 ], or wrist [ 26 ]. Task performance was assessed in 9 studies [ 16 , 20 , 21 , 24 , 25 , 29 , 30 , 34 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

“…Voice data were collected using the microphone of a smartphone in 2 studies [ 20 , 21 ], and 1 study collected voice data using a head-mounted condenser microphone [ 35 ]. Another method of collecting data on symptoms was a self-reported survey [ 20 , 22 , 24 , 26 , 27 , 30 , 34 ]. Structured survey tools for electronic patient-reported outcomes [ 22 ] and ecological momentary assessments (EMAs) [ 26 , 27 ] were developed.…”

Section: Resultsmentioning

confidence: 99%

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Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Yeom¹,

Kim²,

Yoo³

et al. 2022

JMIR Mhealth Uhealth

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Background Self-care is essential for people with Parkinson disease (PD) to minimize their disability and adapt to alterations in physical abilities due to this progressive neurodegenerative disorder. With rapid developments in mobile technology, many health-related mobile apps for PD have been developed and used. However, research on mobile app–based self-care in PD is insufficient. Objective This study aimed to explore the features and characteristics of mobile apps for self-care in people with PD. Methods This study was performed sequentially according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Web of Science, and PsycINFO were searched in consultation with a librarian on June 8, 2021. We used keywords including ”Parkinson disease” and ”mobile.” Results A total of 17 studies were selected based on the inclusion criteria, including 3 randomized controlled trials and 14 observational studies or quasi-experimental studies. The use of mobile apps for self-care in people with PD focused on symptom monitoring, especially motor symptoms. Motor symptoms were objectively measured mainly through the sensors of smartphones or wearable devices and task performance. Nonmotor symptoms were monitored through task performance or self-reported questionnaires in mobile apps. Most existing studies have focused on clinical symptom assessment in people with PD, and there is a lack of studies focusing on symptom management. Conclusions Mobile apps for people with PD have been developed and used, but strategies for self-management are insufficient. We recommend the development of mobile apps focused on self-care that can enhance symptom management and health promotion practices. Studies should also evaluate the effects of mobile apps on symptom improvement and quality of life in people with PD. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42021267374; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021267374.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Yeom¹,

Kim²,

Yoo³

et al. 2022

JMIR Mhealth Uhealth

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“…For example, in case of patients with Parkinson's disease, it can enable tracking anxiety fluctuation, which could help clinicians to administer individualized and effective medication management [5], [6]. It can enable tracking stress in caregivers, especially in case of caregivers managing stress with asthma, and identifying when a caregiver is required to see a psychiatrist to prevent further health complications [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning

Hssayeni

Ghoraani

2021

IEEE Access

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Automated momentary estimation of positive and negative affects (PA and NA), the basic sense of feeling, can play an essential role in detecting the early signs of mood disorders. Physiological wearable sensors and machine learning have a potential to make such automated and continuous measurements. However, the physiological signals' features that are associated with the subject-reported PA or NA may not be known. In this work, we use data-driven feature extraction based on deep learning to investigate the application of raw physiological signals for estimating PA and NA. Specifically, we propose two multimodal data fusion methods with deep Convolutional Neural Networks. We use the proposed architecture to estimate PA and NA and also classify baseline, stress, and amusement emotions. The training and evaluation of the methods are performed using four physiological and one chest motion signal modalities collected using a chest sensing unit from 15 subjects. Overall, our proposed model performed better than traditional machine learning on hand-crafted features. Utilizing only two modalities, our proposed model estimated PA with a correlation of 0.69 (p<0.05) vs. 0.59 (p<0.05) with traditional machine learning. These correlations were 0.79 (p<0.05) vs. 0.73 (p<0.05) for NA estimation. The best emotion classification was achieved by the traditional method with 79% F1-score and 80% accuracy when all the four physiological modalities are used. This is while with only two modalities, the deep learning achieved 78% F1-score and 79% accuracy.

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“…In effect, sensor-based outcomes are often aggregated to match clinical evaluation metrics and time scales, which might account for the current upper limit in wearable classification performance [ 21 , 24 , 29 ]. PD-specific eDiaries [ 56 , 57 , 58 , 59 ] labelled video-recordings on fine time scales [ 60 ], and other virtual telemedicine concepts [ 61 ] may contribute to this challenge.…”

Section: Discussionmentioning

confidence: 99%

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson’s Disease Using a Wrist-Worn Accelerometer

Habets

Kubben

Kuijf

et al. 2021

Sensors

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Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

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Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments

Cited by 18 publications

References 33 publications

Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Use of Mobile Apps for Self-care in People With Parkinson Disease: Systematic Review

Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson’s Disease Using a Wrist-Worn Accelerometer

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