Background: Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders. Summary: For Parkinson’s disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions. Key Messages: Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson’s disease.

Parkinson’s disease is a common neurodegenerative disorder with high prevalence in a worldwide range [1]. A major part of the pathological process involves the degeneration of dopaminergic neurons in pars compacta of substantia nigra, resulting in a significant reduction of dopamine levels in the striatum. Substitution treatment with the dopamine precursor levodopa usually results in a dramatic improvement of all cardinal motor symptoms, including bradykinesia, rigidity, and resting tremor [2]. Unfortunately, as the disease progresses and treatment continues, the initially smooth and continuous therapeutic response starts to become erratic, with the gradual development of fluctuations, freezing of gait, postural instability, and additionally abnormal involuntary movements, usually at the peak of the therapeutic effect. Once present, these motor response complications are there to stay, becoming more and more intense and unpredictable, reducing significantly the quality of life [3].

Treating physicians try to address those problems, by manipulating the time and strength of individual levodopa doses, by using additional medications, or by switching to treatment options for advanced Parkinson’s disease [4]. However, symptoms gradually exacerbate over periods of months or years and also fluctuate from 1 day to the other or even within the same day, making treatment adjustments extremely laborious [5]. So, there is a need for accurate information about the clinical manifestations of the disease, timely delivered to the physicians, aiding them to take the right treatment decisions at the right time. In the current standard of practice, patients may visit their treating physicians once a year or every 3–6 months, while in between, there is little communication. However, this practice does not suit the needs of every individual patient. Some patients have more rapid progression, and they should be assessed probably every month or two, and others are more stable.

Smart monitoring systems and wearable solutions have emerged during the last two decades to complement the in-person medical assessment [6]. In addition, patients with Parkinson’s disease, caregivers, and healthcare professionals have started using such healthcare practices to overcome barriers in the accessibility toward health units [7]. Besides, the need for objective symptoms’ detection that could effectively direct treatment decisions cannot always be satisfied by clinical evaluation, since this is subjective to physicians’ experience and expertise, and even commonly used rating scales may be susceptible to wide inter- and intra-ratter variability [8]. Enhancement of monitoring systems has made it possible for wearable devices to accurately capture the motor symptoms of Parkinsonism, supporting objective patients’ evaluation [9‒11]. A further step for wearable technology toward “precision medicine” in Parkinson’s disease is continuous monitoring with data collection at home environment, providing a detailed analysis of the patient’s clinical state throughout the day, while performing their usual everyday activities as well as a quantitative assessment of the patient’s journey during long periods of months and years. These medical solutions are in line with the existing standard of care, albeit improving it and supporting a paradigm shift compared to today’s practice.

Since their launching, wearable monitoring systems are constantly improving their effectiveness in parkinsonian symptom detection. Tremor was one of the first symptoms to be captured by wearables with electromyographic sensors and/or accelerometers [12‒14]. At the same time, bradykinesia was reported to be effectively quantified with ambulatory monitoring, as well as dyskinesia, and activity of patients with Parkinson’s disease [15‒17]. However, the first sensors and algorithms developed were focused on the detection of specific symptoms, not being able to depict the wide range of motor disturbances of PD. Based on the detected symptoms and the variation of their severity throughout each day, motor fluctuations with wearing-off could be evaluated. But still, the estimation in off time was biased by the number of the recorded symptoms.

The evolution of digital technologies led to the development of wearable devices that could track more symptoms accurately [18‒20]. For the gait analysis on the other hand, while sensors were implemented in measurement of gait parameters and general activity quite early [21], it took more time to be implemented in the detection of gait impairment in PD [22]. Especially for freezing of gait, being a debilitating motor phenomenon in PD, algorithms evolved gradually to manage to identify it among different gait traits or even to predict it [23‒26]. Similarly for postural instability, a significant indicator assessing the risk of falls, machine learning techniques managed during the last decade to depict it, adding this measurement in the range of parkinsonian symptoms detected by wearable sensors [27, 28].

The evolution of monitoring devices has finally led to systems that can record all the cardinal symptoms of PD along with motor complications, including measurements from four extremities and the trunk, and giving an integrated evaluation of patient’s condition [9, 29‒32]. Each monitoring system is validated against clinical examination, to confirm that it delivers relevant and correct information. There are also systems for clinical use depending on one detector only and, despite the fact that there are no comparative studies, it seems likely that monitoring all four extremities and the trunk gives an integrated picture of the patients’ symptomatology.

Many patients with advanced Parkinson’s disease, especially those suffering from other comorbidities, face difficulties in their access to healthcare units, mainly due to motor complications and gait impairment. These barriers became more evident during the last 2 years of COVID-19 pandemic [33]. Camera-based telemedicine consultations and other telemedicine services emerged as a solution [34], but for Parkinson’s disease, these modules do not always allow physicians to capture the full status of the patients, since they don’t offer a full 3D view, and like the office visit, they are restricted in a short time frame. For advanced Parkinson’s disease, where people often cannot reach their treating physician, telemedicine empowered by wearable devices has turned out to be very helpful. The use of wearable monitoring systems that provide accurate measurement of all motor symptoms can support personalized adjustments in the delivery of care [7, 29].

In the classical medical practice, physicians, during the usually short office visit, try to extrapolate the overall situation of the patient by the clinical evaluation from neurological examination and scales. They also try to gather information about patients’ status at home, by asking them and/or the caregivers to recall their situation for every hour of the day, and describe periods of impaired mobility, the existence of dyskinesias, and other symptoms, or ask the patients to fill manual diaries. This information is biased among others by the patients’ memory capacity, and their mood status, leading often to overestimation or underestimation of symptoms [35]. Diaries may provide some help, but a recent study validating house diaries filled by patients compared with ones filled by expert Parkinson’s disease nurses proves that there is a poor agreement and that patients report inadequately the actual motor states, especially dyskinesia [36]. Undoubtedly, having a manual diary of the patient is one step. Yet, having an objective monitoring is better and of course having monitoring in the home environment during common activities of daily living is probably the golden standard, which is expected to provide more accurate follow-up and vastly improve the treatment of patients and the coordination of services [31].

Parkinson’s disease, being a neurodegenerative disorder, has a long and slowly progressive course, for most of the patients. In this journey each patient passes from one stage of the disease to the next with gradual exacerbation of motor and nonmotor symptoms, over periods of months and years [37, 38]. Variations in symptom severity also exist between different days or even hours of the day. So, physicians need to keep track of these changes to be able to make timely interventions and promptly adapt medications in the patients’ needs [39]. Clinical examination and PD scales have been proven to show low reliability in the evaluation of changes during disease progression [40, 41]. On the other hand, machine learning analysis of wearable sensor records can discriminate distinct stages of PD, offering a reliable solution for objective monitoring of disease progression [42].

Wearable systems designed for use at home, with sensors accommodated in the trunk and both the upper and lower extremities, present an evolution of wearable devices because they allow continuous, precise, and bilateral monitoring, as often as needed, supporting proactive and preventive disease monitoring and treatment optimization [7]. This is expected to improve the medical care, and it is significantly better than the implementation of similar devices that are only used as a Holter, which create objective diaries linked only with specific, routine visits, and not in a continuous and proactive manner, as is the case of monitoring systems for a long-term use at home. Of course, Holter use is an option, but more continuous use gives an even better picture, when resources allow that. This kind of continuous, objective monitoring gives a much better possibility for the effective adjustment of any Parkinson’s therapy, because of the valuable information it offers about the patient’s status and symptom fluctuations over time, which is so important for treatment optimization.

Continuous objective monitoring can lead to the early detection of symptoms and fluctuations in patients that are not yet aware of their existence, or that they cannot explain/understand what exactly is happening to them. Early detection and timely treatment of motor fluctuations are expected to improve their possibilities to live a normal life or to stay in work and be effective in work for longer periods, with a serious impact on the patients’ quality of life and the health economics of the system [43, 44]. The detection of gait-related symptoms, such as freezing of gait and postural instability, are key components when we try to optimize pharmacological and nonpharmacological treatment in Parkinson’s disease [45]. These are symptoms that also have a strong effect on the quality of life. Therefore, wearable systems monitoring gait impairment among other symptoms fulfill an unmet need in the evaluation and treatment of patients with Parkinson’s disease [46]. More so, even in the late-stage Parkinson’s disease, patients continue to have both motor and nonmotor fluctuations, though perhaps of a lower amplitude than earlier in the disease. A monitoring system will continue to be very useful even in this late stage of the disease, since there is always a need for treatment optimization, which is the closest possible to permanent cure of the disease [47].

Better understanding of the distribution and fluctuation of symptoms throughout the day may eventually lead to optimization of the treatment, which is also expected to improve the management of levodopa-induced dyskinesia. There are patients who are quite unhappy with mild dyskinesia, and others seem to tolerate even moderately severe one. Most will exhibit peak-dose dyskinesia, but some will manifest end-of-dose or biphasic and then of course the situation is completely different [48]. All this is happening in different moments of the day, with differing intensity and duration, but when the physicians actually see the patient, they only see one snapshot, missing the whole picture. Accordingly, having charts that show in a clinically meaningful way the various conditions of the patient throughout the day, including fluctuations and dyskinesias, becomes quite important, presumably improving the current management of Parkinson’s disease. So far, physicians had to decide for the medication changes based on their inner ranking and subjective opinion, that is, difficult to adapt to the needs of the patients. On the other hand, monitoring systems with wearables that provide objective measurements of dyskinesia in both upper and lower extremities, bilaterally, comprise a new approach offering an earlier and more accurate diagnosis of the severity and body/limb distribution of the symptoms, offering opportunities for better treatment choices, which could potentially improve patient’s quality of life [49].

For the intensified therapy options, the number of choices is actually increasing, as we will possibly have subcutaneous levodopa [50] and other forms of continuous delivery or a closed-loop deep brain stimulation [51], together with the existing levodopa-carbidopa (with or without entacapone) intestinal gel pump, deep brain stimulation, and apomorphine options [52]. Currently, there is an underuse of advanced therapies and a reason behind this is that physicians have difficulties to identify the right patient candidates [53]. Many centers for Parkinson’s disease already use objective monitoring in the screening of patients for advanced therapy [54]. This will probably be used more frequently in the future because it offers a more precise base for the decision about the need for invasive therapy and the suitable type of invasive therapy, offering also a valuable support to decision‐makers in state and private insurances. At the same time, the follow-up of a patient in advanced therapy will be more effective with objective monitoring, leading to clear decisions about adjusting a treatment to get the optimal effect of it, or stopping it if the right result cannot be attained, oftentimes leading to a change from secondary to primary delivery of care [7, 55]. Furthermore, there is currently some confusion, regarding these choices and, sooner or later, the medical authorities or insurance companies will require the doctors to provide an objective validation about the stratification of patients to one of these invasive and expensive treatments [56, 57]. Probably, providing an objective assessment will soon be very important in obtaining reimbursement in all health systems. The appropriate use of wearable monitoring devices in the above context is expected to benefit the system from an economic standpoint.

Despite significant technological advancements and the incorporation of new features in monitoring devices, wearables still face certain limitations. One major pitfall lies in their accuracy in symptom detection, as there is considerable variability in sensitivity and specificity, ranging from 0.39 to 0.97, across different devices and study designs [9, 55, 58‒60]. It is not surprising that objective measurements do not always align perfectly with subjective symptom estimations, but the correlation between supervised and unsupervised motor function assessments is also not flawless [61]. The inherent intra- and inter-rater variability in clinical scales, on which wearables' algorithms are based, inevitably affects their performance. However, despite these limitations, monitoring devices can still record symptoms with sufficient accuracy to support clinical decisions [62].

Moreover, the acceptance of monitoring systems by patients, caregivers, and healthcare professionals remains an important issue, but it is often poorly measured and reported in most studies [63]. Patients’ expectations for improved assessment of symptoms and better communication with treating physicians, potentially leading to more informed treatment decisions, appear to be the driving force behind the use of wearables, but the ease of use is also very important [64, 65]. Conversely, patients with PD emphasize on the weight of their communication with the physicians in decision-making processes [66]. Unfortunately, this communication was reported to be limited, even in crucial issues like ON/OFF fluctuations, in traditional medical care approaches [67, 68], and there are legitimate concerns that the widespread adoption of digital health tools may lead to a potential disintegration of the patient-physician relationship, which is highly valued by patients [69, 70].

There are still challenges that wearable technologies have to address in order to meet the needs of both patients and health professionals. Achieving high accuracy in detecting all motor symptoms is a fundamental requirement for the successful integration of wearable technologies into routine medical practice. Precision, consistency, and reliability are indispensable factors in the ongoing advancement of closed-loop systems that utilize real-time symptom data to automate treatment adjustments [71]. Adopting a user-centered design approach that involves patients, caregivers, and physicians in the development of wearable systems’ features is expected to enhance user engagement and compliance [64].

Moreover, efficient management of PD depends on the detection of nonmotor symptoms and biomarkers. Wearable devices capable of real-time measurement of levodopa levels, combined with systems that capture motor symptoms, could significantly enhance the available information for optimizing treatment decisions [72, 73]. The incorporation of continuous blood pressure measurements into these systems could provide valuable insights into autonomic dysfunction, aiding in the effective management of orthostatic hypotension [74, 75]. Advancements in sleep and cognitive disorder technologies are also rapidly progressing [76‒78], making their integration into future smart systems for comprehensive detection of PD symptoms a feasible possibility.

Across disease stages and therapy types, objective monitoring allows for a proactive disease management since it gives information about a patient’s personal situation earlier than physicians would otherwise detect it, making it possible to give them a really individualized treatment. Finally, it prevents the patients from falling into periods of unnecessary suffering and the disease from exacerbating, by making an optimized treatment possible at an earlier stage. Instead of the doctor waiting in his office until the patient visits him with an already obviously established situation, there will be a paradigm shift toward a more proactive, preventive, and personalized approach by the regular use of remote objective monitoring at home. Especially systems detecting an array of motor symptoms in both upper and lower extremities, and assessing accurately gait impairment, will pave the way for this paradigm shift, leading to improved patient quality of life, through treatment optimization at all times.

The authors have no conflicts of interest to declare.

The authors did not receive external funding for this manuscript.

Conceptualization: K.I.T., P.O., A.A., H.R., and S.K.; investigation and writing – original draft preparation: K.I.T.; writing – review and editing: P.O., A.A., H.R., and S.K.; and supervision: S.K.

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