Web-based telemonitoring and delivery of caregiver support for patients with Parkinson disease after deep brain stimulation: protocol

Caregivers' burden and deep brain stimulation for Parkinson disease: A systematic review of qualitative studies

BACKGROUND AND PURPOSE

The impact of subthalamic nucleus deep brain stimulation (STN-DBS) on caregivers' burden is understudied. We perform a systematic review and meta-synthesis aggregating qualitative studies involving partners of people with Parkinson disease (PwP) to explore their experiences and unmet needs.

METHODS

A systematic review for retrieving qualitative studies included six databases: MEDLINE, Embase, CINAHL, Cochrane, PsycInfo, and Scopus. Inclusion criteria were as follows: (i) studies on the experience of caregivers of PwP in the context of STN-DBS, (ii) English peer-reviewed articles, and (iii) qualitative or mixed methods studies reporting caregivers' quotations. After the appraisal of included studies, we performed meta-synthesis of qualitative findings. Descriptive themes and conceptual elements related to PwP partners' experiences and unmet needs were generated.

RESULTS

A total of 1108 articles were screened, and nine articles were included. Three categories were identified: (i) dealing with Parkinson disease (PD) every day (the starting situation characterized by the impact of PD on ordinary life; the limitations to partners' socialization; partners' efforts in stepping aside for love and care activities), (ii) facing life changes with STN-DBS (the feeling of being unprepared for changes; the fear and concern due to loved ones' behavioral changes; struggling to find an explanation for those changes), and (iii) rebuilding the role of caregiver and partner after STN-DBS.

CONCLUSIONS

This meta-synthesis elucidates concerns, challenges, and unmet needs of partners of PwP who underwent STN-DBS. It is important to provide them with information, education, and adequate support to face these challenges. Professionals need to involve partners in the care and decision process, because STN-DBS-related outcomes do not depend solely on the well-being of PwP but also on the well-being of individuals surrounding them.

Caregiver Burden in Partners of Parkinsonian Patients with Deep Brain Stimulation

In Parkinson’s disease (PD) patients, the progressive nature of the disease and the variability of disabling motor and non-motor symptoms contribute to the growing caregiver burden of PD partners and conflicts in their relationships. Deep brain stimulation (DBS) improves PD symptoms and patients’ quality of life but necessitates an intensified therapy optimization after DBS surgery. This review illuminates caregiver burden in the context of DBS, framing both pre- and postoperative aspects. We aim to provide an overview of perioperative factors influencing caregiver burden and wish to stimulate further recognition of caregiver burden of PD patients with DBS.

Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study

BACKGROUND

Facial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to "hypomimia" or "masked facies."

OBJECTIVE

We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD.

METHODS

We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD.

RESULTS

The algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda's seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7).

CONCLUSIONS

This proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient's motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine.

An update on adaptive deep brain stimulation in Parkinson's disease

Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Advances in closed-loop deep brain stimulation devices

BACKGROUND

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

METHODS

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

RESULTS

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

CONCLUSIONS

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.