Application of telehealth intervention in Parkinson’s disease: A systematic review and meta-analysis

Abstract

Introduction

Telehealth intervention has been proposed as a sustainable and innovative intervention approach to Parkinson’s disease (PD) patients, but there are still conflicting results in the literature about its effect. This study aimed to evaluate the efficacy of telehealth intervention for PD patients.

Methods

PubMed, EMBASE, CENTRAL and China National Knowledge Infrastructure (CNKI) were searched from the inception to June 2018 for randomized controlled trials (RCTs) and cohort studies, without language restrictions. When feasible, data were statistically pooled for meta-analysis using Review Manager 5.3. Otherwise, narrative summaries were used.

Results

Twenty-one studies were included. With respect to PD severity, compared with usual care, telehealth intervention was beneficial in lowering motor impairment of PD patients significantly (mean difference (MD) = –2.27, 95% confidence interval (95% CI) −4.25 to −0.29, p = 0.02), rather than mental status (MD = –0.98, 95% CI −2.61 to 0.65, p = 0.24), activities of daily living (MD = –1.51, 95% CI −4.91 to 1.89, p = 0.38) and motor complications (MD = –0.36, 95% CI −1.31 to 0.59, p = 0.46). Telehealth intervention did not lead to significant reduction in quality of life (standardized mean difference (SMD) = 0.04, 95% CI −0.20 to 0.28, p = 0.76), depression (SMD = –0.12, 95% CI −0.37 to 0.13, p = 0.34), cognition (MD = 0.37, 95% CI −0.34 to 1.09, p = 0.31) and balance (MD = 0.09, 95% CI −2.49 to 2.66, p = 0.95).

Discussion

Telehealth intervention is an effective option for individuals with PD to improve their motor impairment. Further well-designed studies are warranted to confirm our findings.

Keywords

Parkinson’s disease telehealth intervention systematic review meta-analysis

Introduction

As the global population ages, Parkinson’s disease (PD) has today become the second most common neurodegenerative disease, after Alzheimer’s disease,¹ affecting about 6.2 million people globally.² PD brings patients both motor and non-motor symptoms: the former include postural instability, rigidity, bradykinesia, gait deficits and resting tremor,³ while non-motor symptoms include anxiety, depression and sleep disturbance.⁴ These symptoms always result in poor quality of life (QOL), increased health care costs and elevated caregivers’ burden.⁵

In spite of these adverse symptoms, the literature indicates that a great number of PD patients have limited access to conventional face-to-face healthcare due to factors such as a distance barrier, financial burden, mobility difficulties, or lack of time.⁵ More than 40% of PD patients did not receive specialized care from neurologists at the early stage after diagnosis,⁶ resulting in potential health risk. Therefore, sustainable health care is very clinically important for these patients, in that it may alleviate patients’ PD severity and increase their QOL level.⁷

With the clinical application of electronic information equipment, and given the weaknesses of conventional health care, telehealth intervention has been proved to be an innovative mode of health care for those patients in remote locations.⁸ Telehealth intervention is defined as rapid information communication between medical workers and patients through telecommunication technology.⁹ Previously published researches have demonstrated that telehealth intervention is feasible and beneficial in improving access to medical care for patients with various diseases, such as breast cancer,¹⁰ cardiac disease,¹¹ and diabetes mellitus.¹² With respect to PD, although a number of randomized controlled trials (RCTs) and cohort studies have looked at the clinical effect of telehealth intervention, no consistent conclusions have yet been drawn. Some articles reported that telehealth intervention encouraged a reduction in motor impairment severity,^7,13 while others did not find significant change.^14,15 Furthermore, no relevant meta-analysis on this aspect has been previously reported.

The present study aimed to synthesize and present the latest available evidence on the effect of telehealth intervention on PD patients.

Methods

The meta-analysis was performed following several guidelines, including the Cochrane Handbook, the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Informed consent and ethical approval were deemed exempt because no clinical intervention was conducted on patients.

Literature search

PubMed, EMBASE, the Cochrane Central Register of Controlled Trails (CENTRAL) and China National Knowledge Infrastructure (CNKI) were searched from inception to June 2018, with no language restrictions. Medical subject headings and free terms were used together in the process of literature search, including (telehealth OR mHealth OR telemedicine OR eHealth OR telecare OR mobile health OR health, mobile OR phone OR text message OR short message OR mobile application OR mobile technology OR smart phone technology OR mobile apps OR computer OR internet OR interactive video intervention OR video recording OR videoconferencing OR videoconference) AND (Parkinson’s disease OR Parkinson disease OR Parkinson OR Parkinsonism OR Paralysis Agitans OR PD). Moreover, we also reviewed the references of screened articles and reviews to identify other relevant investigations.

Study selection

We included studies if they were RCTs, cohort studies or case-control studies; recruited participants with PD, assessed the effect of a telehealth intervention technique and reported pre-defined outcomes. Pre-defined outcomes included PD severity, QOL, depression, cognition and balance.

Data extraction and quality assessment

Two researchers independently performed data extraction and quality assessment, and disagreements were resolved by consultation with a third researcher. For each of the eligible studies, a prespecified data extraction form was adopted to extract the following data, including study characteristics (study year, country, study design, sample size and duration of follow-up), patient characteristic (age), comparisons (telehealth intervention vs conventional face-to-face healthcare, cohort studies without comparison), and outcomes (PD severity, QOL, depression, cognition and balance). When related data were missing, we would contact the authors if possible. The Cochrane Risk of Bias Assessment Tool was used for quality assessment of RCTs, and the Newcastle-Ottawa Scale was used for cohort studies.

Statistical analysis

All the statistical analyses were conducted using Review Manager 5.3. Mean difference (MD) or standardized mean difference (SMD) along with corresponding 95% confidence interval (95% CI) were pooled for continuous variables appropriately, according to whether the outcomes were measured by the same scales or not. When mean ± standard deviations (SDs) were not given, they would be calculated by other available data. The Cochran Q-statistic and I² statistic were used to evaluate the between-study heterogeneity. P value < 0.1 along with I²> 50% indicate significant heterogeneity and a random-effects model would be chosen to pool results; otherwise, a fixed-effects model would be used.

We conducted the following two set of analyses: (1) a systematic review of follow-up outcomes of telehealth intervention based on data reported in RCTs and cohort studies; (2) a meta-analysis of telehealth intervention vs conventional face-to-face healthcare on the basis of RCTs.

Results

Search results

The process of study selection is shown in Figure 1. The database searches yielded 3106 articles and three trials were added through other sources. After removal of duplicate publications, 2273 titles and abstracts were screened. Of these, 1997 were excluded after reading the abstracts, leaving 276 for the full-text review. Finally, 21 articles met all the inclusion criterion, of which 11 articles^8,15–24 were RCTs and 10 articles were cohort studies.^{7,13,14,25–31}

Figure 1.

PRISMA flow diagram of study selection.

Table 1 shows the baseline characteristics of included studies, involving 903 PD patients. Among these studies, the types of technology used for telehealth intervention were different. Thirteen studies adopted computer-based interventions, and seven studies adopted phoned-based interventions. The intervention duration ranged from 30 days to 34 months. The quality assessments of the included studies are displayed online in Supplemental Table 1, Supplementa1 Table 2 and Supplementa1 Table 3.

Table 1.

Characteristics of included trials.

Trial	Location	Study design	No. of participants	Age (years, experimental/ control)	Interventions	Technology	Control	Duration	Outcome and outcome measure
Beck 2017	America	RCT	195 (97/98)	66.4 ± 8.1 (65.9 ± 7.8/ 66.9 ± 8.5)	Usual care supplemented by 4 virtual visits via video conferencing from a remote specialist	Computer	Usual care	1 year	Mental status (MDS-UPDRS), Activities of daily living (MDS-UPDRS), Motor severity (MDS-UPDRS), Motor complications (MDS-UPDRS), Cognition (MoCA)
Cubo 2017	Spain	RCT	38 (18/20)	66.44 ± 7.09/ 66.05 ± 9.76	Home-based motor monitoring by using wireless motion sensor technology	Phone	Usual care	1 year	Mental status (UPDRS), Activities of daily living (UPDRS), Motor severity (UPDRS), Motor complications (UPDRS), Depression (HADS)
Heldman 2017	America	RCT	18 (9/9)	65.2 ± 10.1/ 68.6 ± 10.2	Telehealth management of Parkinson’s disease using wearable sensors	Videoconference or phone	Usual care	3 months	Mental status (UPDRS), Activities of daily living (UPDRS), Motor severity (UPDRS), Motor complications (UPDRS), Quality of life (PDQ-39)
Lakshminarayana 2017	England	RCT	201 (94/107)	60.31 ± 9.73 (59.86 ± 9.13/ 60.71 ± 10.26)	Smartphone-based Parkinson’s tracker app	Phone	Usual care	16 weeks	Quality of life (PDQ-39), Depression (HADS)
Khalil 2017	Jordan	RCT	30 (16/14)	58.4 ± 13.5/ 60.7 ± 15.4	The home use of an exercise DVD, walking programme and initial instructional sessions and weekly phone calls provided by a physiotherapist	Phone	Usual care	8 weeks	Motor severity (MDS-UPDRS), Balance (Mini BESTest)
Wilkinson 2016	America	RCT	36 (18/18)	67.2 ± 9.8/ 70.9 ± 8.4	Clinical video telehealth	Computer	Usual care	12 months	Motor severity (mUPDRS), Quality of life (PDQ-8), Depression (GDS)
Patel 2016	America	RCT	28 (14/14)	63.1 ± 6.8/ 64.7 ± 9.5	A 6-week web-based course consisting of daily ‘lessons’ providing learnable skills and appropriate recommendations to help patients improve their sleep habits and patterns	Computer	Usual care	6 weeks	Mental status (UPDRS), Activities of daily living (UPDRS), Quality of life (PDQ-8), Depression (PHQ-9)
Ginis 2016	Belgium	RCT	38 (20/18)	67.30 ± 8.13/ 66.11 ± 8.07	A smartphone application offered positive and corrective feedback on gait	Phone	Usual care	6 weeks	Motor severity (UPDRS), Quality of life (SF-36), Cognitive score (CTT-A), Balance (Mini BESTest)
Theodoros 2016	Australia	RCT	31 (15/16)	71.62 ± 7.77/ 72.86 ± 9.99	Online Lee Silverman Voice Treatment	Computer	Usual care	1 month	Quality of life (PDQ-39)
Dorsey 2013	America	RCT	20 (9/11)	66.6 ± 4.0/ 64.5 ± 3.4	Care from a specialist delivered remotely at home	Computer	Usual care	7 months	Motor severity (UPDRS)
Dorsey 2010	America	RCT	10 (6/4)	71.7 ± 7.9/ 69.8 ± 7.0	Telemedicine care with a movement disorder specialist	Computer	Usual care	6 months	Motor severity (UPDRS), Cognition (MoCA)
Seidler 2016	America	Cohort studies with control group	20 (10/10)	68.1 ± 7.9 /68.9 ± 9.4	A telerehabilitation approach to group tango instruction	Computer	Usual care	12 weeks	Motor severity (MDS-UPDRS), Balance (BESTest)
Naismith 2013	Australia	Cohort studies with control group	50 (35/15)	68.5 ± 7.1/ 64.9 ± 6.5	Cognitive training was computer-based and was conducted by clinical neuropsychologists.	Computer	Usual care	7 weeks	Depression (BDI)
Veazey 2009	America	Cohort studies without control group	5	70.8 ± 8.6	Eight weekly phone calls from a therapist	Phone	–	8 weeks	Quality of life (PDQ-39)
Butterfield 2017	America	Cohort studies without control group	34	66.0 ± 10.7	A primarily phone-based, 6-week activity scheduling and monitoring intervention	Phone	–	6 weeks	Activities of Daily Living (UPDRS), Quality of life (PDQ-39), Depression (GDS)
Dibilio 2017	Italy	Cohort studies without control group	7 (4/3)	63.6 ± 8.4	Twice a week for 6 weeks with 1-hour sessions of computer-assisted training of attention ability and information processing tasks	Computer	–	6 weeks	Motor severity (UPDRS)
Barbour 2016	Panama	Cohort studies without control group	16	80.63 ± 6.521	Telehealth intervention, PD patients were seen virtually in follow-up over a 3-year period by Telehealth Services	Computer	–	7 to 34 months	Mental status (UPDRS), Activities of Daily Living (UPDRS), Motor severity (UPDRS)
Kraepelien 2015	Sweden	Cohort studies without control group	9	66.0 ± 11.6	Internet-based cognitive behavioural therapy	Computer	–	12 weeks	Quality of life (PDQ-8), Depression (HADS)
Milman 2014	Israel	Cohort studies without control group	18 (11/7)	67.7 ± 6.4	Home-based computerized cognitive training programme	Computer	–	12 weeks	Cognitive score (Mindstreams battery of Computerized neuropsychological tests)
Marzinzik 2012	Germany	Cohort studies without control group	78 (44/34)	67 ± 8	Patients send video recordings made in the home to the treating team via the internet	Computer	–	30 days	Motor severity (UPDRS)
Dobkin 2011	America	Cohort studies without control group	21 (8/13)	65.86 ± 9.32	Phone-based cognitive-behavioural therapy	Phone	–	10 weeks	Depression (BDI)

BDI: Beck Depression Inventory; BESTest: Balance evaluation systems test; CTT-A: Color Trail Test A; GDS: Geriatric Depression Scale; HADS: Hospital Anxiety Depression Scale; MDS-UPDRS: Movement Disorder Society – sponsored revision of the Unified Parkinson’s Disease Rating Scale; Mini BESTest: the mini-Balance Evaluation Systems Test; MoCA: Montreal Cognitive Assessment; mUPDRS: modified Unified Parkinson’s Disease Rating Scale; PDQ: Parkinson’s Disease Questionnaire; PHQ: Patient Health Questionnaire; SF-36: the short Form 36 Health Survey; UPDRS: Unified Parkinson’s Disease Rating Scale.

Systematic review

A total of 18 studies reported both baseline and follow-up outcomes of telehealth intervention.^{7,8,13–15,17–20,23–31} These studies were conducted in populations from America, Spain, England, Jordan, Belgium, Australia, Italy, Panama, Sweden, Israel and Germany. Various types of technology were investigated, with the performance of computer-based intervention in 10 studies, and phone-based intervention in 7. The intervention duration in each included study ranged from 30 days to 34 months. Most studies had an intervention duration of 6 weeks. In total, 678 participants were involved in the systematic review. The largest sample size, 201 PD patients, was reported in the study conducted by Lakshminarayana et al.¹⁷

Table 2 showed the studies with available data on variation of PD severity level. In these studies, PD severity was evaluated by the Unified Parkinson’s Disease Rating Scale (UPDRS) or its modified version. UPDRS includes four dimensions: mental status, activities of daily living, motor severity and motor complications. Four studies reported the outcome of mental status, and most studies found that the score reduced after intervention. Five studies reported the change of activities of daily living, with the score ranging from 7.0 ± 5.40 to 16 ± 6.282 at baseline, and 8.0 ± 4.67 to 19.5 ± 7.589 at follow-up. Among nine studies that reported the score of motor severity, reduction was found in five studies,^7,8,13,18,27 with significant reduction in two.^7,13 In addition, two studies reported the score of motor complications, and slight reduction without statistical significance was only shown in one study.^15,23

Table 2.

PD severity of included studies.

	Mental status		Activities of daily living		Motor severity		Motor complications
Study	Baseline	Follow-up	Baseline	Follow-up	Baseline	Follow-up	Baseline	Follow-up
Cubo 2017	2.94 ± 1.83	2.53 ± 1.7	14.06 ± 4.66	14.24 ± 5.92	28.93 ± 12.81	33.24 ± 11.41	5.83 ± 1.86	5.41 ± 2.57
Heldman 2017	7.0 ± 2.96	8.0 ± 2.59	7.0 ± 5.40	8.0 ± 4.67	21.0 ± 12.37	25.0 ± 19.4	0.5 ± 2.59	4.0 ± 2.59
Khalil 2017	–	–	–	–	52.6 ± 16.8	41.4 ± 18	–	–
Wilkinson 2016	–	–	–	–	19.9 ± 12.9	8 ± 4.7	–	–
Patel 2016	9.8 ± 4	8.1 ± 4.2	12.6 ± 7.9	12.5 ± 7	–	–	–	–
Ginis 2016	–	–	–	–	28.35 ± 14.77	30.85 ± 14.26	–	–
Seidler 2016	–	–	–	–	34.5 ± 7.78	29 ± 8.74	–	–
Butterfield 2017	–	–	15.42 ± 6.48	14.79 ± 5.91	–	–	–	–
Dibilio 2017	–	–	–	–	25 ± 6.1	24.1 ± 6.5	–	–
Barbour 2016	4.06 ± 3.235	3.5 ± 2.708	16 ± 6.282	19.5 ± 7.589	20.06 ± 9.849	25.56 ± 9.899	–	–
Marzinzik 2012	–	–	–	–	31.2 ± 8.9	24 ± 9.5	–	–

The studies that reported on changes of QOL, depression, cognition and balance are shown in Table 3. Of these, in total eight studies reported QOL with different measures. Butterfield et al. investigated the effects of telehealth intervention using the scale of Parkinson’s disease questionnaire-39 (PDQ-39) in 34 patients, and found a significant improvement of QOL after intervention.²⁶ Eight studies reported depression level before and after telehealth intervention, and four of these showed significant reduction after intervention.^19,26,28,30 Among the two studies reporting the change of cognitive function, Milman et al. found that telehealth intervention can improve cognitive function of PD patients significantly.²⁹ The change of balance was reported in three studies. In two, telehealth intervention appeared to increase the level of balance for PD patients although the difference did not reach statistical significance, whereas Ginis et al. found that telehealth intervention significantly improved balance at post-test, although the effect did not remain at follow-up.²⁰

Table 3.

Quality of life, depression, cognition and balance of included studies.

	Quality of life			Depression			Cognition			Balance
Study	Baseline	Follow-up	Outcome measures	Baseline	Follow-up	Outcome measures	Baseline	Follow-up	Outcome measures	Baseline	Follow-up	Outcome measures
Cubo 2017	–	–	–	13.33 ± 5.72	13.81 ± 5.74	HADS	–	–	–	–	–	–
Heldman 2017	12.8 ± 3.26	17.6 ± 7.11	PDQ-39
Lakshminarayana 2017	154.53 ± 27.98	155.47 ± 28.16	PDQ-39	5.15 ± 3.68	5.26 ± 3.73	HADS	–	–	–	–	–	–
Khalil 2017	–	–	–	–	–	–	–	–	–	17.7 ± 5.5	19.6 ± 5.6	Mini BESTest
Wilkinson 2016	–	–	–	3.8 ± 4.3	3.3 ± 4	GDS	–	–	–	–	–	–
Patel 2016	14.4 ± 4.4	14.2 ± 4.3	PDQ8	7.8 ± 5.1	5.8 ± 5.5	PHQ-9	–	–	–	–	–	–
Ginis 2016	58.59 ± 18.6	59.46 ± 17.13	SF-36	–	–	–	73.65 ± 34.67	64.3 ± 29.87	CTT-A	24.75 ± 5.61	24.95 ± 4.78	Mini BESTest
Theodoros 2016	22.8 ± 12.9	21.5 ± 10.4	PDQ-39
Seidler 2016	–	–	–	–	–	–	–	–	–	86.5 ± 15.56	92.5 ± 9.04	BESTest
Naismith 2013	–	–	–	8 ± 5	8.2 ± 5.3	BDI	–	–	–	–	–	–
Veazey 2009	39.8 ± 10.9	37.2 ± 18.6	PDQ-39	–	–	–	–	–	–	–	–	–
Butterfield 2017	30.82 ± 15.18	27 ± 14.8	PDQ-39	13.61 ± 7.04	10.2 ± 6.5	GDS	–	–	–	–	–	–
Kraepelien 2015	37.2 ± 14.6	37.2 ± 20	PDQ-8	10.6 ± 2.7	7.8 ± 2.5	HADS	–	–	–	–	–	–
Dobkin 2011	–	–	–	22.62 ± 9.24	11.89 ± 9.73	BDI	–	–	–	–	–	–
Milman 2014	–	–	–	–	–	–	87.3 ± 9.2	95.1 ± 7.8	Mindstreams battery of computerized neuropsychological tests	–	–	–

BDI: Beck Depression Inventory; BESTest: balance evaluation systems test; CTT-A: Color Trail Test A; GDS: Geriatric Depression Scale; HADS: Hospital anxiety and depression scale; Mini BESTest: the mini-Balance Evaluation Systems Test; PDQ: Parkinson’s disease questionnaire; PHQ: Patient Health Questionnaire; SF-36: the short Form 36 Health Survey.

Meta-analyses

PD severity

Four studies were included for the meta-analysis of mental status, four for activities of daily living, eight for motor severity, and three for motor complications (Figure 2). The fixed-effects model was selected to pool results, except for the meta-analysis of mental status (p = 0.003, I²= 79%) and activities of daily living (p = 0.03, I²= 66%). The results revealed that telehealth could improve PD patients’ motor impairment, with statistical difference (MD = –2.27, 95% CI −4.25 to −0.29, p = 0.02). But telehealth intervention did not lead to significant reduction in the score of mental status (MD = –0.98, 95% CI −2.61 to 0.65, p = 0.24), activities of daily living (MD = –1.51, 95% CI −4.91 to 1.89, p = 0.38), and motor complications (MD = –0.36, 95% CI −1.31 to 0.59, p = 0.46).

Figure 2.

Forest plot of effects of telehealth intervention on: (a) mental status, (b) activities of daily living, (c) motor severity and (d) motor complications.

Quality of life

Five trials involving 270 participants reported the outcome of QOL (Figure 3). The effect measure SMD was selected because of different measurement scales. We identified significant homogeneity across these studies (p = 0.59, I²= 0%), so a fixed-effects model was used to summarize mean effect size. It was found that the effect of telehealth intervention was not statistically significant (SMD = 0.04, 95% CI −0.20 to 0.28, p = 0.76).

Figure 3.

Forest plot of effects of telehealth intervention on quality of life (QOL).

Depression

Four articles reported depression level before and after intervention (Figure 4). No significant heterogeneity was found between studies (p = 0.90, I2 = 0%), so the fix-effect model was used. The pooled result indicated that the change in depression level did not differ between the telehealth intervention and usual in-person care (SMD = −0.12, 95%CI −0.37 to 0.13, p = 0.34).

Figure 4.

Forest plot of effects of telehealth intervention on depression.

Cognition

Two studies with 205 patients were enrolled in this meta-analysis calculating the cognitive function (Figure 5). MD was selected in this analysis because of the same measurement scale. There was homogeneity across these two studies (p = 0.79, I²= 0%), so a fixed-effects model was selected for analysis. We found that the effect of telehealth intervention was not obvious (MD = 0.37, 95% CI −0.34 to 1.09, p = 0.31).

Figure 5.

Forest plot of effects of telehealth intervention on cognition.

Balance

Two studies were included in the meta-analysis of balance, with 64 patients overall (Figure 6). Between-study homogeneity was found (p = 0.87, I²= 0%), and the result of a fixed-effects model indicated that the use of telehealth intervention did not result in a statistically significant change in balance (MD = 0.09, 95% CI −2.49 to 2.66, p = 0.95).

Figure 6.

Forest plot of effects of telehealth intervention on balance.

Discussion

As the population ages and people’s requirements for health care grow, increasing access to health care has become an important issue worldwide. However, clinic-based programmes are inaccessible for many patients due to distance, disability, and the distribution of physicians.³² The application of telehealth intervention in remote patient monitoring and management technologies has been regarded as one of the top fifty priority topics for comparative research.²² Today, the use of telehealth intervention in neurology, previously known as teleneurology, is increasing.^9,33 It is reported that more than 85% of neurology departments in the USA use or will use telehealth in clinical practice.³⁴ Although the benefits of telemedicine-based health care programmes have been introduced in a number of medical contexts, related data in the field of PD are scarce.⁷ To the best of our knowledge, this study represents the first meta-analysis to explore and quantitatively analyse the effect of telehealth intervention on PD patients. The results revealed that, compared with usual care, telehealth intervention could improve PD patients’ motor impairment effectively, although no significant difference was found in the score of mental status, activities of daily living, motor complications, QOL, depression, cognition and balance between the two groups.

PD patients always experience a series of symptoms, of which motor impairments such as tremor, slowness of movement and muscle rigidity are the most common.² For these patients, one main treatment strategy is to reduce the severity of motor symptoms. Treatment adjustments in PD are mainly dependent on motor assessments. Compared with conventional in-person care, PD patients can communicate through telehealth care with physicians in a more timely manner, enabling the medical team to identify the specific needs of an individual PD patient and then provide a personalised rehabilitation approach.¹⁴ This is the reason, we found, that telehealth is beneficial in improving motor impairment. The lack of significant improvement in QOL, depression, cognition and balance levels could be attributed to several factors. For example, a small study size may have insufficient power to detect potentially meaningful differences between the telehealth and in-person groups. Other reasons include insufficient telehealth visits with medical workers, required reliance on local clinicians to implement the specialist’s suggestions, and choices in the different measure scales used.¹⁶

With the development and evolvement of telehealth technologies, the opportunities for patients to access care will increase; it is important for us to understand the advantages, disadvantages and problems surrounding telecare. Telehealth intervention is a highly effective and accessible intervention which can increase practice outreach, and continue medical education for patients.³⁵ In comparison with conventional in-person care, telehealth intervention could eliminate the need to travel distances and reduce the need for parking areas, waiting lounges and clinic space.¹⁶ In addition, telehealth helps to reduce travel time, and is associated with lower overall health costs.¹⁴ Furthermore, the care provided by telehealth intervention is always performed in patients’ home environment, where patients are more relaxed and likely to perform rehabilitation exercise better.¹⁴ Overall, patients are more satisfied with telehealth intervention and prefer it over in-person care.¹⁶ It is reported that self-management support and shared decision-making are the two effective ways to support PD patients.³⁶ PD patients generally need more mental support from medical professionals and wish to participate in clinical decision-making more actively.³⁶ Telehealth can provide exactly such an opportunity for patient involvement. However, lack of adherence to telehealth care was found among some PD patients, who did not want to attract their family members’ attention or to have to explain the rehabilitation exercises at home.¹⁸ Therefore, during telehealth care, multidisciplinary telehealth care should be adopted, rather than only a neurologist, in order to increase patients’ attendance.

In spite of the clinical benefits, telehealth intervention has faced some challenges that deserve attention. Firstly, there is the issue of user retention; some user interfaces and user experiences are not suitable for PD patients, so they seldom open the application after they have downloaded it.¹⁷ Therefore, we should make a suitable choice of telecommunication technology for patients, according to their condition, such as video conferencing, phone-based intervention, computer-based intervention, home-based motor monitoring or centre-based motor monitoring, and with an optimal duration and frequency of intervention. Another, larger, challenge concerns the inconsistency and insufficiency of financial reimbursement and insurance coverage.^22,37 Some countries, including Germany and Denmark, provide reimbursement for telemedicine services.²² These countries always have a single government payer or a centralized medical records system. However, elsewhere this is not the case: in the United States for example, telehealth is reimbursed by Medicare health insurance in designated Centers within rural areas, but is not in many larger communities, despite their population size.²² Other challenges include related lack of neurologists and dependency on equipment.⁹

There are some limitations in the current study. First, the number of available RCTs for meta-analysis was small, and the majority of them had small sample sizes. Second, different characteristics were shown in the included 21 studies. For instance, the intervention duration in most studies was short-term (<1 year), and limited studies provided long-term follow-up data. These differences would lead to between-study homogeneity. Also, there is scarce data in the literature about PD patients in developing countries and the implementation of functional training programmes.¹⁸

Conclusion

Telehealth intervention is an effective option for individuals with PD to improve their motor impairment. Further well-designed studies are warranted to confirm our findings.

Supplemental Material

Supplemental material for Application of telehealth intervention in Parkinson’s disease: A systematic review and meta-analysis

Supplemental material for Application of telehealth intervention in Parkinson’s disease: A systematic review and meta-analysis by Yan-Ya Chen, Bing-Sheng Guan, Ze-Kai Li, Qiao-Hong Yang, Tian-Jiao Xu, Han-Bing Li and Qin-Yang Wu in Journal of Telemedicine and Telecare

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the Medical Scientific Research Foundation of Guangdong Province of China [grant number B2018044].

Supplemental Material

Supplemental material is available for this article online.

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