Effectiveness of remote physiotherapeutic e-Health interventions on pain in patients with musculoskeletal disorders: a systematic review

Abstract

Purpose

To systematically review the literature on effectiveness of remote physiotherapeutic e-Health interventions on pain in patients with musculoskeletal disorders.

Materials and methods

Using online data sources PubMed, Embase, and Cochrane in adults with musculoskeletal disorders with a pain-related complaint. Remote physiotherapeutic e-Health interventions were analysed. Control interventions were not specified. Outcomes on effect of remote e-Health interventions in terms of pain intensity.

Results

From 11,811 studies identified, 27 studies were included. There is limited evidence for the effectiveness for remote e-Health for patients with back pain based on five articles. Twelve articles studied chronic pain and the effectiveness was dependent on the control group and involvement of healthcare providers. In patients with osteoarthritis (five articles), total knee surgery (two articles), and knee pain (three articles) no significant effects were found for remote e-Health compared to control groups.

Conclusions

There is limited evidence for the effectiveness of remote physiotherapeutic e-Health interventions to decrease pain intensity in patients with back pain. There is some evidence for effectiveness of remote e-Health in patients with chronic pain. For patients with osteoarthritis, after total knee surgery and knee pain, there appears to be no effect of e-Health when solely looking at reduction of pain.

Implications for rehabilitation

• This review shows that e-Health can be an effective way of reducing pain in some populations.

• Remote physiotherapeutic e-Health interventions may decrease pain intensity in patients with back pain.

• Autonomous e-Health is more effective than no treatment in patients with chronic pain.

• There is no effect of e-Health in reduction of pain for patients with osteoarthritis, after total knee surgery and knee pain.

Keywords:

• Physiotherapy
• musculoskeletal
• e-Health
• systematic review
• pain

Introduction

Musculoskeletal disorders contain over 150 diagnoses from the locomotor system and are prevalent in over 50% of the adult population [1–3]. The global disability-adjusted life years (DALYs) for musculoskeletal disorders increased by 61.6% between 1990 and 2016, making this the second largest problem in terms of years lived with disability [1]. Pain is the most common complaint in patients with a musculoskeletal disorder. This musculoskeletal pain leads to a decrease in functioning on daily basis [4].

Physiotherapists are specialized in treating musculoskeletal disorders and support patients to increase their daily functioning [5]. The focus of physiotherapeutic treatment in patients with musculoskeletal pain is on offering individualised patient care to develop strategies for managing pain, rehabilitate and restore physical function by delivering education, exercises, and encouragement to engage in regular physical activity [6]. Physiotherapists may use e-Health in their treatment, as e-Health can serve as a supportive tool for education, exercises, and encouragement [7].

E-health can be defined as the delivery of personalized healthcare at a distance through the use of technology [8]. This technology can be computers and mobile phones, but also satellite communications [9]. When smart or portable devices are specifically used, for instance using mobile applications, this often is referred to as mobile health (mHealth) [9]. Besides the mode of delivery (i.e., computer, mobile, tablet), the moment of delivery can also vary. For instance, e-Health can be used in both the diagnostic process (e.g., intake, questionnaires) [10] and therapeutic process (e.g., videoconferencing, online exercises, education) [11–14]. The types of e-Health interventions vary from interactive websites, to internet delivered programs, and to mobile applications. Furthermore, there are different ways e-Health can be implemented: as an addition to face-to-face care (i.e., blended care), or as a complete replacement for physical consults [13,15,16].

In several countries including the Netherlands during the 2019 novel coronavirus (COVID-19) pandemic [17,18], all physiotherapeutic care had to be offered at distance. By applying remote e-Health tools, patients with (chronic) pain still received care during this pandemic. From the available literature it is known that e-Health generally can be just as effective or slightly more effective than usual face-to-face care [19,20]. However, the challenge remains the uncertainty about the effectiveness in different populations [18,19,21]. It is currently still unknown what the effectiveness is of these remote e-Health interventions on patients with musculoskeletal pain-complaints. Therefore, the aim of this study was to systematically review the literature on the effectiveness of remote physiotherapeutic e-Health interventions on pain in patients with musculoskeletal disorders.

Methods

Protocol and registration

Reporting of this review has been done according to PRISMA recommendations [22], and is registered on PROSPERO (registration number CRD42018055386).

Eligibility criteria

Randomized controlled trials of remote physiotherapeutic e-Health interventions in adults with complaints of the musculoskeletal system were included. Inclusion criteria for the studies applied were: (1) the primary or secondary outcome measure had to be pain related (e.g., pain intensity, pain days, pain-related disability); and e-Health interventions must be: (1) remotely transmitted, (2) applied within the field of physiotherapy for patients with musculoskeletal complaints. The inclusion criteria show that interventions that are within the physiotherapeutic domain (e.g., counselling, exercises) [23] are included as long as patients do not go to the clinic to receive their physiotherapeutic treatment, but their physiotherapeutic treatment is given at distance through an e-Health application (interactive websites, internet delivered programs, video consultations or mobile applications). The therapy could consist of exercises, [24] pain education [25,26] or any other intervention that aimed at decreasing pain-related complaints, for example encouragement to engage in regular physical activity.

Exclusion criteria applied were: (1) the e-Health intervention is a complement to usual face-to-face physiotherapeutic care (e.g., blended care), (2) the e-Health intervention is not patient-oriented (i.e., educational intervention for healthcare professionals), (3) the outcome measures were incomplete for baseline or follow-up, (4) the full text article is not available in English or Dutch, and (5) feasibility studies of e-Health.

Information sources

Literature searches were performed in the electronic databases PubMed [1966 − 2022], Embase [1947 − 2022], and Cochrane [1898 − 2022] to identify randomized controlled trials examining the effects of e-Health interventions on patients with musculoskeletal complaints. Database searches were performed on 25 February 2022.

Search

Combinations of keywords, free text and medical subject headings related to telemedicine, Internet, e-Health interventions, musculoskeletal disorders and physiotherapy were the core of the search strategy. The search strategy was developed in PubMed (Appendix Table A1) and thereafter fitted for other online databases. Keywords and medical subject heading terms used in the search were: (1) e-health OR e-Health OR web OR Internet OR online OR computer OR telemedicine OR telerehabilitation, AND (2) musculoskeletal disorders OR physiotherapy, AND (3) intervention OR randomized controlled trial. Screening of articles was done by two researchers (HvdM, ST), a third researcher (CMS) screened those articles with no consensus independently to be included or excluded. The process of screening was based on relevance and pre-set inclusion and exclusion criteria. References of remaining articles were screened for additional studies. The search results were downloaded in a database created using Covidence [27].

Table 1. Best evidence synthesis.

Strong evidence	Provided by consistent, statistically significant findings in outcome measures in at least two high quality RCTs†
Moderate evidence	Provided by consistent, statistically significant findings in outcome measures in at least one high quality RCT and at least one low quality RCT or high quality CCT†
Limited evidence	Provided by statistically significant findings in outcome measures in at least one high quality RCT†, or provided by consistent, statistically significant findings in outcome measures in at least two high quality CCTs† (in the absence of high quality RCTs)
Indicative findings	Provided by statistically significant findings in outcome and/or process measures in at least one high quality CCT or one low quality RCT† (in the absence of high quality RCTs), or provided by consistent, statistically significant findings in outcome and/or process measures in at least two ODs with sufficient quality (in absence of RCTs and CCTs)†
No evidence	In cases of results of eligible studies that do not meet the criteria for one of the above-stated levels of evidence, or in case of conflicting results among RCTs and CCTs, or in case of no eligible studies

RTCs: randomized controlled trials; CCTs: controlled clinical trials; ODs: other designs.

†If the proportion of studies that show evidence is <50% of the total number of studies within the same category of methodological quality and study design (RCTs, CCTs, or ODs), we state no evidence.

Study selection

Inclusion criteria were applied to the titles and abstracts of all retrieved records by two researchers blinded for each other’s results (ST, HvdM) after removal of duplicates. For eligibility this process was repeated for full text screening of the remaining articles. A third reviewer (CMS) made the decision regarding inclusion of the article when the two reviewers did not reach consensus.

Data collection process, data items and summary measures

Two reviewers (ST, TvB) performed data extraction from the included articles. A third reviewer (HvdM) checked the extracted data for accuracy. The following key data were extracted from the included articles: (1) study characteristics: sample size; (2) participant characteristics: age and gender; (3) intervention & control characteristics: type of (control) intervention(s), frequency, follow-up of interventions; and (4) outcome measures. For the follow-up data the longest available term was included in this systematic review. Mean and standard deviations were derived from the included studies, where possible, for further statistical analysis. When the p-values for the between-group results were not available, these were calculated using the mean and standard deviations from the publication.

Risk of bias in individual studies

The Cochrane “risk of bias” (RoB) tool assessment was used to assess the risk of bias of the included studies. The RoB tool assesses whether a study has high, low or unclear risk of bias, which may influence study validity, across six domains [28]. The tool does not lead to a quality score but solely focuses on internal validity [28]. Two researchers (HvdM, ST) assessed the methodological quality and risk of bias independently. In case of disagreement, a third reviewer (CMS) made the decision. Results of the RoB assessment were also presented graphically individually and in a summary graph using Review Manager (RevMan) [29]. For the assessment of methodological quality, the Physiotherapy Evidence Database scale (PEDro) was used. The PEDro scale is a valid instrument to measure the methodological quality of clinical trials [30]. The PEDro scale consists of 11 items of which ten of the items assess the internal and/or external validity of the article [31]. The sum score of the ten items is used to rate the methodological quality of the article. An article can score a 9–10 (excellent); 6–8 (good); 4–5 (moderate); or 0–3 (poor) [31].

Synthesis of results

Meta-analysis of the results was not possible due to heterogeneity. So, the evidence was weighed based on the present methodological quality of the included studies. A best evidence synthesis was applied, based on the criteria as described by van Tulder et al. (Table 1) [32]. The synthesis of the between-group results were described per patient population, as well as the level of involvement of the healthcare provider in the intervention group. This level could be when patients had online interactions with their healthcare provider, described as minimal involved, or when patients had no interaction at all with their healthcare provider. Finally, a distinction was made in the comparison of the intervention with the control group. The control group was subdivided into no care (e.g., waiting list, assessment only) or usual care (e.g., face-to-face treatment).

Additional analysis

For each study, within-group effect sizes were calculated according to the standard mean difference [33]. Effect sizes (ES) were classified as small (<0.20), moderate (around 0.50) or large (>0.80), according to Cohen’s criteria [34]. For between-group analysis the p-value was calculated.

Results

Study selection

The search strategy revealed 11,811 initial articles from PubMed, Pedro and Cochrane (Figure 1). Two articles were identified through hand search. After removing double references, 10, 424 titles and abstracts were screened for eligibility, resulting in 201 articles which were screened full-text. Finally, 27 articles were included [35–62], and 175 were excluded because inclusion criteria were not met on the study design (n = 41), the patient population (n = 44), the intervention (n = 53), the outcome measures (n = 19), or other reasons (n = 18). A list of excluded references can be found in Appendix 2.

Figure 1. Flowchart of the study.

Study characteristics

Overall, the intervention groups consisted of 2,311 patients with a range in age of 35 to 68 years (Table 2). Five articles focused on back pain [36,45,52,61,62], twelve on musculoskeletal chronic pain [38,40–42,46,48,51,53,57–60], five on osteoarthritis [35,43,44,47,54], two on total knee surgery [49,50] and three articles on knee pain [37,55,56]. Applied e-Health interventions were interactive websites (n = 8) [36,38,40,46,47,49,52,62], internet delivered programs (n = 15) [35,37,41–43,45,48,50,51,56–60] and mobile applications (n = 5) [44,53–55,61]. Study duration ranged from 6 weeks to 12 months. Six studies [41,49,50,53,58,60] measured pain intensity by the Visual Analog Scale (VAS), a 100 mm line (0 mm: no pain; 100 mm: extreme pain) [64]. Nine studies [35,37,38,42,43,54,56,57,61] measured pain intensity by the Numeric Pain Rating Scale (NPRS), an 11-point scale (0: no pain; 10: extreme pain). Two studies [36,46] used the pain severity subscale of the Multidimensional Pain Inventory (MPI) [65]. Five studies [40,48,51,59,62] used the Brief Pain Inventory (BPI) [66]. Furthermore, three studies [43,50,56] used the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) [67] pain subscale, one study used the Arthritis Impact Measurement Scale 2 (AIMS2) [68] pain subscale [47], one study [55] PROMIS pain and one study [44] used the Hip Disability and Osteoarthritis Outcome Score (HOOS) [69] and Knee Injury and Osteoarthritis Outcome Score (KOOS) [70].

Table 2. Study characteristics.

		Intervention group			Control group				Pain outcome
Author, year	Complaints							Follow-up	Measurement tool	Within-group						Between- group
		Population	Intervention		Population	Control intervention				Intervention group			Control group
		N (%F) mean age ± SD	Description	Study duration	N (%F) mean age ± SD	Description	Study duration			B m (SD)	FU m (SD)	ES Cohen’s d	B m (SD)	FU m (SD)	ES Cohen’s d	p-Value
Back pain
Buhrman, 2004 [36]	Low back pain	21 (64) 44 ± 10	Internet-based cognitive-behavioral intervention with telephone support	8 wk	29 (62) 45 ± 11	Waiting-list crossover to intervention	N.A.	3 mo	MPI	3.8 (1.9)	3.0 (1.3)	0.5	5.0 (1.7)	3.1 (1.2)	1.3	0.78
									Pain diary avg.	37.4 (18.2)	36.2 (20.4)	−0.1	44.4 (14.2)	32.6 (21.6)	−0.7	0.56
Chiauzzi, 2010 [62]	Chronic back pain	95 (67) 47 ± 12	Self-guided website with lessons, interactive tools, personalized assessments, and articles	4 wk	104 (68) 45 ± 12	Participants were emailed a back-pain guide, they were asked to read the guide	4 wk	6 mo	BPI	5.6 (0.2)	4.8 (0.3)	−3.6	5.6 (0.2)	5.2 (0.2)	−2.1	0.00*
Krein, 2013 [45]	Low back pain	111 (11) 51 ± 13	Internet-mediated walking program with pedometer	12 mo	118 (14) 52 ± 13	Uploading pedometer and monthly e-mail reminders	N.A.	12 mo	NPRS	6.0 (1.9)	5.4 (2.2)	−0.3	6.1 (1.6)	5.6 (2.0)	−0.3	0.81
Lorig, 2002 [52]	Low back pain	190 (39) 47± –	Closed, moderated e-mail discussion group including a book and video about back pain	12 mo	231 (40) 45± -	Subscription to a non-health related magazine of personal choice	12 mo	12 mo	NPRS	4.0 (2.4)	2.5 (2.6)	−0.6	3.8 (2.4)	2.8 (2.6)	−0.4	0.05*
Toelle, 2019 [61]	Non-specific low back pain	48 (73) 41 ± 11	Multidisciplinary mHealth back pain app	3 mo	46 (67) 43 ± 11	Six individual physiotherapy sessions	6 wk	12 wk	NPRS	5.1 (1.1)	2.7 (1.5)	−1.8	5.4 (1.2)	4.1 (1.4)	−1.0	0.02*
Musculoskeletal chronic pain
Burke, 2019 [57]	Chronic pain, spinal cord injury	35 (29) 50 ± 12	Internet delivered cognitive behavioural therapy	12 wk	34 (21) 52 ± 14	No intervention	N.A.	12 wk	NPRS	5.3 (2.1)	5.0 (2.2)	−0.1	4.4 (2.2)	4.6 (2.3)	0.1	0.02*
Buhrman, 2013 [46]	Chronic pain	36 (72) 40 ± 9	Online cognitive behavioral training and education program	8 wk	36 (72.2) 40 ± 9	A moderated online discussion forum	8 wk	8 wk	MPI	3.8 (1.0)	3.7 (1.1)	−0.1	4.0 (1.1)	4.2 (1.2)	0.1	0.07
Calner, 2017 [58]	Persistent musculoskeletal pain	55 (86) 44 ± 10	Self-guided, web-based programme &MMR	16 wk	44 (84) 42 ± 11	Multimodal rehabilitation (MMR)	N.A.	12 mo	VAS	66.1 (16.7)	56.6 (20.7)	−0.5	64.7 (16.2)	57.3 (22.5)	−0.4	0.89
Friesen, 2017 [59]	Fibromyalgia	30 (93) 49 ± 10	Internet delivered cognitive behavioural pain management cours	8 wk	30 (97) 46 ± 13	Waiting list	N.A.	8 wk	BPI	5.5 (1.1)	5.0 (1.7)	−0.3	6.0 (1.4)	6.3 (1.3)	0.2	0.00*
Gialanella, 2017 [60]	Chronic neck pain	47 (89) 56 ± 14	Home based telemedicine program with scheduled phone calls	6 mo	47 (89) 60 ± 11	Recommendation to continue exercising at home	N.A.	6 mo	VAS	6.8 (1.3)	3.9 (1.8)	−1.9	6.6 (1.5)	5.1 (1.9)	−0.9	0.00*
Hernando-Garijo, 2021 [41]	Fibromyalgia	17 (100) 52 ± 9	Telerehabilitation aerobic exercise program	15 wk	17 (100) 55.±9	Usual care	N.A.	15 wk	VAS	7 (1.5)	4.9 (2)	−1.2	7.3 (1.1)	6.5 (1.9)	−0.5	0.02*
Kroenke, 2014 [48]	Chronic pain	124 (12) 55 ± 9	Telecare management	12 mo	126 (22.2) 55 ± 8	Pain care as usual from their primary care physicians	12 mo	12 mo	BPI	5.3 (1.8)	3.6 (2.2)	−0.9	5.1 (1.8)	4.6 (2.1)	−0.3	<0.00*
Lorig, 2008 [38]	Arthritis and Fibromyalgia	441 (90) 52 ± 11	Web-based self-management program with moderated online workshops	6 wk	425 (91) 53 ± 12	Usual care	N.A.	12 mo	NPRS	6.1 (2.4)	5.8 (2.5)	−0.1	6.4 (2.2)	6.5 (2.3)	0.1	0.00*
Rickardsson, 2021 [42]	Chronic pain	57 (72) 48 ± 13	iACT – a novel format of Internet-ACT using exercises	8 wk	56 (79) 51 ± 11	Waiting list	N.A.	Post treatment	NPRS	5.2 (1.5)	3.7 (0.3)	−1.4	5.8 (1.5)	5.4 (0.3)	−0.4	0.00*
Smith, 2019 [40]	Chronic pain	41 (90)	Web-based self-mangement program	16 wk	39 (84)	Usual care	16 wk	16 wk	BPI	5.4 (1.7)	4.44 (1.6)	−0.6	5.05 (1.7)	4.73 (1.7)	−0.2	0.42
Wilson, 2015 [51]	Persistent Pain	45 (37) 49 ± 12	Internet-based self-management program	8 wk	47 (41) 50 ± 11	Delay (wait-list comparison).	N.A.	8 wk	BPI	5.6 (1.6)	5.3 (1.9)	−0.2	5.0 (1.7)	5.1 (1.8)	0.1	0.60
Yuan, 2021 [53]	Fibromyalgia	20 (95) 43.3 ± 8.4	Mobile app	6 wk	20 (100) 42.1 ± 11.8	Paper book	6 wk	6 wk	VAS	5.9 (2.2)	5.1 (2.6)	−0.33	5.7 (2.2)	5.3 (2.3)	−0.18	0.798
Osteoarthritis
Anthony, 2022 [54]	Osteo-arthritis, waiting list for knee or hip surgery	45 (37.8) 63	Acceptance and commitment therapy (ACT) delivered via a mobile phone	2 wk	54 (33.3) 65	Assessment only	2 wk	2 wk	NRPS	5.4 (2.1)	5.5 (2.2)	0.05	5.9 (1.9)	6.2 (2.2)	0.15	0.22
Bossen, 2013 [35]	Osteo-arthritis	100 (60) 61 ± 5.9	Fully automated Web-based intervention that contains automatic functions without human support	9 wk	99 (69) 63 ± 5.4	Waitling list	9 wk	12 mo	NRPS	5.4 (1.2)	3.5 (1.1)	−1.7	4.9 (1.2)	3.8 (1.1)	−0.9	0.33
Gohir, 2021 [43]	Osteo-arthritis	48 (71) 65.2 ± 9.7	Digitally delivered program with daily exercises and informative texts	6 wk	57 (65) 68 ± 8.6	Usual care	N.A.	6 wk	NRPS	4.4 (2.0)	2.6 (N.A)	(N.A)	4.7 (2.1)	4.3 (N.A)	(N.A)	<.00*
									WOMAC Pain	8.0 (3.9)	5.8 (N.A)	(N.A)	7.8 (3.7)	6.6 (N.A)	(N.A)	0.02*
Pelle 2020 [44]	Osteo-arthritis	214 (69) 62.1 ± 7.7	Standalone eHealth application	N.A.	213 (75) 62.1 ± 7	Usual care	N.A.	6 mo	HOOS/KOOS	57.5 (15.5)	59.4 (17.7)	0.1	58.2 (17.8)	57.5 (18.0)	−0.1	0.27
Rini, 2015 [47]	Osteo-arthritis	58 (79) 69 ± 8	Internet-based self-management program.	8 wk	55 (82) 67 ± 11	Assessment only	6-9 wk	Post-intervention	AIMS2	4.8 (1.7)	4.1 (2.0)	−0.4	5.1 (1.8)	4.6 (1.8)	−0.3	0.17
Total knee surgery
Piqueras, 2013 [49]	Total Knee	72(83) N.A.	Interactive virtual telerehabilitation system	2 wk	70 (63) N.A.	Conventional out-patient physiotherapy	N.A.	3 mo	VAS	3.8 (2.0)	2.0 (2.0)	−0.9	4.3 (1.9)	2.0 (2.5)	−1.0	1.00
Russell, 2011 [56]	Total Knee	31 66 ± 8	Internet-based telerehabilitation system	6 wk	34 70 ± 7	Usual face-to-face	N.A.	6 wk	WOMAC Pain	4.3 (2.7)	3.0 (2.3)	−0.6	3.7 (1.8)	2.2 (1.8)	−0.8	0.24
									VAS	4.4 (1.4)	3.1 (1.6)	−0.9	5.2 (1.5)	3.3 (1.3)	−1.3	0.22
Knee pain
Bennell, 2017 [63]	Knee pain	74 (58) 61 ± 7	Internet-delivered physiotherapist prescribed home exercise and pain coping skills training	N.A.	74 (54) 62 ± 8	Online education	N.A.	9 mo	NPRS	6.1 (1.4)	3.6 (2.2)	−1.4	6.2 (1.3)	4.7 (2.5)	−0.8	0.00*
									WOMAC pain	9.0 (2.4)	5.1 (2.9)	−1.5	9.2 (2.5)	6.9 (3.5)	−0.8	<0.00*
Gruner, 2021 [55]	Knee pain	31 (50) 58.5 ± 3.7	Digital exercise therapy application	8 wk.	29 (34.6) 55.9 ± 13.3	Conventional physiotherapy	8 wk	8 wk	PROMIS_PI	58.8 (6.7)	52.7 (6.8)	−0.9	57.0 (5.3)	55.5 (7.5)	−0.23	0.14
Kim, 2016 [44]	Knee pain	284 (61) 52	Online exercises	6 wk	286 (61) 52	Static handout of exercises	6 wk	6 wk	NPRS	4.4 (2.2)	3.9 (2.0)	−0.2	3.9 (1.7)	3.7 (1.8)	−0.1	0.11

B: baseline; BPI: Brief Pain Inventory; ES: effect size; F: female; FU: follow-up; m: mean; mo: month(s); MPI: multidimensional pain inventory; NA; not applicable N: number of participants; NPRS: numeric pain rating scale; SD: standard deviation; VAS: visual analogue scale; Wk: week(s); *significance level ≤0.05.

Risk of bias and methodological quality within studies

Of the six bias domains assessed with the Cochrane RoB tool (selection bias, performance bias, detection bias, attrition bias, reporting bias and other bias), all except attrition bias had at least 25% unclear or high risk of bias (Figure 2). Attrition bias and detection bias had the lowest risk of interfering with the results, and detection bias had a 100% high risk of bias score throughout as patients were the outcome assessors for the outcome measure “pain intensity.” Figure 3 depicts the RoB per domain for each individual study. All included studies scored a high risk of bias on at least two domains. Seven studies received a high risk of bias on three or more domains and they also had minimally one other domain with an unclear risk of bias [36,46,48,51,52,61,62]. Two studies did not have any low risk of bias scores [51,62].

Figure 2. Risk of bias graph.

Risk of bias graph: review authors’ judgements about each risk of bias item presented as percentages across all included studies. Created with RevMan 5.4.1[29].

Figure 3. Risk of bias summary.

Risk of bias summary: review authors’ judgements about each risk of bias item for each included study. Created with RevMan 5.4.1.[29].

Table 3 depicts the PEDro scores per study. The scores for methodological quality ranged from 5 to 8 points, which indicates that all included studies are of moderate to good methodological quality. None of the studies, except for one [37], blinded the participants or therapists and all reported between-group statistical comparisons and point measures for key outcomes.

Table 3. Methodological quality assessment based on the PEDro scale.

Author, year	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Total score
Anthony, 2022	y	1	1	1	0	0	0	1	1	0	1	06/10
Benell, 2017	y	1	1	1	0	0	0	1	1	1	1	07/10
Bossen, 2013	y	1	1	1	0	0	0	0	1	1	1	06/10
Buhrman, 2004	y	1	0	1	0	0	0	1	0	1	1	05/10
Buhrman, 2013	y	1	0	1	0	0	0	0	1	1	1	05/10
Burke, 2019	y	1	0	1	0	0	1	1	0	1	1	06/10
Calner, 2017	y	1	1	1	0	0	0	0	0	1	1	05/10
Chiauzzi, 2010	y	1	0	1	0	0	0	1	1	1	1	06/10
Friesen, 2017	y	1	1	1	0	0	0	1	0	1	1	06/10
Gialanella, 2017	y	1	0	1	0	0	0	1	0	1	1	05/10
Gohir, 2021	y	1	1	1	0	0	0	0	1	1	1	06/10
Gruner, 2021	y	1	1	1	0	1	1	1	1	1	1	09/10
Hernando-Garijo, 2021	y	1	0	1	0	0	1	1	1	1	1	07/10
Kim, 2016	y	1	1	1	0	1	1	0	1	1	1	08/10
Krein, 2013	y	1	1	1	0	0	1	1	1	1	1	08/10
Kroenke, 2014	y	1	1	1	0	0	1	1	1	1	1	08/10
Lorig, 2008	y	1	0	1	0	0	0	0	1	1	1	05/10
Lorig, 2002	y	1	0	1	0	0	0	0	1	1	1	05/10
Pelle, 2020	y	1	1	1	0	0	0	0	1	1	1	06/10
Piqueras, 2013	y	1	0	1	0	0	1	0	0	1	1	05/10
Rickardsson, 2021	y	1	1	1	0	0	0	1	1	1	1	07/10
Rini, 2015	y	1	1	1	0	0	1	1	1	1	1	08/10
Russell, 2011	y	1	1	1	0	0	1	1	1	1	1	08/10
Smith, 2019	y	1	1	1	0	1	1	1	1	1	1	09/10
Toelle, 2019	y	1	0	1	0	0	0	1	0	1	1	05/10
Wilson, 2015	Y	1	0	1	0	0	1	1	0	1	1	06/10
Yuan, 2021	y	1	1	1	0	0	1	1	1	1	1	08/10

Q1: eligibility criteria were specified (n = no, y = yes; not taken into consideration for total score); Q2: subjects were randomly allocated to groups (in a crossover study, subjects were randomly allocated an order in which treatments were received); Q3: allocation was concealed; Q4: the groups were similar at baseline regarding the most important prognostic indicators; Q5: there was blinding of all subjects; Q6: there was blinding of all therapists who administered the therapy; Q7: there was blinding of all assessors who measured at least one key outcome; Q8: measures of at least one key outcome were obtained from more than 85% of the subjects initially allocated to groups; Q9: all subjects for whom outcome measures were available received the treatment or control condition as allocated or, where this was not the case, data for at least one key outcome was analyzed by “intention to treat”; Q10: the results of between-group statistical comparisons are reported for at least one key outcome; Q11: study provides both point measures and measures of variability for at least one key outcome. For all items 0 = no, 1 = yes.

Results of studies per patient population

The results of the individual studies and the synthesis of the results are described below per patient population. The between-group effects are used for synthesis of the results as described in the methods. All results are depicted in Table 2.

Back pain

Three studies of moderate methodological quality [36,52,61] and two studies of good methodological quality [45,62] assessed the effectiveness of remote e-Health on pain intensity in patients with back pain. Three articles used the NPRS [45,52,61], one study used BPI [61], and one study used both MPI and the VAS through a pain diary [36].

Two studies [36,52] investigated minimally involved care through e-Health and compared their interventions to no care. One study [52] of moderate methodological quality investigated an interactive website which contained a closed moderated discussion group including a book and video about back pain, which was significantly effective on the NPRS compared to the control group [52]. The other study [36], also of moderate methodological quality, used an interactive website for an online self-help program with telephone support as intervention. Compared to the control group, there was no significant effect found on the MPI [36]. Due to inconsistent between-group findings, moderate methodological quality and high risk of bias was found in both studies [36,52]; there is currently no evidence that e-Health with minimal involvement of a healthcare provider is more effective than no care in patients with back pain.

Three studies [45,61,62] described full online programs without healthcare provider involvement. One study [45] compared a group receiving an internet delivered physical activity program where patients received activity trackers, with a group that would upload pedometer data and received email updates. This study was of high methodological quality, low risk of bias and found no statistical difference between both groups on the NPRS. Another study [62] compared an interactive website for an online self-help program to a back pain guide. This study was of good methodological quality, had a high risk of bias and found a significant effect in favour of the intervention on the BPI. These two studies [45,62] compared the online intervention to reading material, but showed conflicting results. The third study [61] which was of moderate methodological quality and had a high risk of bias compared a mobile application to face-to-face physiotherapy and found a significant effect in favour of the mobile application. As this is a moderate quality study, there are indicative findings that e-Health is more effective than face-to-face therapy for patients with back pain.

Overall, there is limited evidence that remote e-Health is more effective than a control intervention on pain intensity in patients with back pain.

Chronic pain

Five studies [38,46,51,58,60] of moderate methodological quality and six studies [41,42,48,53,57,59] of good methodological quality and one article [40] of excellent quality assessed the effectiveness of remote e-Health in patients with chronic or persistent pain. Four studies [40,48,51,59] used the BPI total intensity score, one study [46] used the MPI, three studies [38,42,57] used the NPRS and the VAS was used in four studies [41,53,58,60].

Ten of the interventions used were online with no involvement of the healthcare provider and consisted of internet delivered programs or interactive websites with cognitive behavioural aspects [42,46,51,57,59], telecare management [41,48,53] or a self-guided web-based program [40,58]. Four studies [40,41,48,58] compared their intervention to face-to-face interventions, of which two studies [41,48] found a significant effect for the intervention group, but the other two studies [40,58] did not. Therefore, there is currently no evidence that remote e-Health without involvement of the healthcare provider is more effective than face-to-face interventions in patients with chronic or persistent pain [40,41,48,58]. Four other studies [42,51,57,59] compared their intervention to no intervention and three studies [42,57,59] found a significant difference, indicating that there is moderate evidence for the effectiveness of remote e-Health in patients with chronic or persistent pain when compared to no intervention [42,51,57,59]. One study [46] compared e-Health to an online discussion forum and one study [53] compared e-Health to an paper book, and neither study found statistical differences between groups.

Two studies described e-Health interventions with minimal involvement of a healthcare provider, including telecare management [38,60]. They compared the intervention to usual care, of which both [38,60] found a significant result in favour of the intervention. These findings suggest that there is strong evidence for e-Health interventions with minimal involvement in patients with chronic or persistent pain when compared to a control group (usual care or online).

Osteoarthritis

Five studies [43,44,47,54,71] looked at the effect of different remote e-Health interventions on pain in patients with osteoarthritis. These articles were all assessed as good methodological quality (Table 3) and consists of internet-based self-management program with minimal involvement of a healthcare provider [43,47] and a fully automatic web-based program or mobile application for patients with osteoarthritis [35,44,54]. Pain intensity was measured with AIMS2 [47], NPRS [35,43,54], WOMAC [43], and HOOS/KOOS [44]. Three interventions [35,47,54] were not significantly more effective than no care and therefore there is currently no evidence for the effectiveness of remote e-Health on pain intensity in patients with osteoarthritis compared to no care. Two studies compared the e-Health intervention to usual care, of which one study found a statistical between-group difference [43] and the other study did not [44]. Therefore, there is currently no evidence for the effectiveness of remote e-Health on pain intensity in patients with osteoarthritis compared to usual care.

Total knee surgery

Two studies [49,50] looked at remote tele-rehabilitation with minimal involvement of a healthcare provider after total knee surgery. The studies were of moderate [49] and high [50] methodological quality and showed no significant improvement on pain measured with the VAS [49,50] and WOMAC [50] when compared to no care. Therefore, there is moderate evidence that e-Health is not effective on pain intensity for patients after total knee surgery compared to no care.

Knee pain

Three studies [37,55,56] investigated on pain outcome in a population of knee pain with online modules measured with NPRS, WOMAC and PROMIS and compared this intervention to a handout with exercises [37], online education [56] and conventional physiotherapy [55]. Two studies [37,56] had good methodological quality and one study [55] with excellent quality showed conflicting results on significance compared to their control group. Therefore, there is limited evidence that e-Health is effective on pain intensity for patients with knee related complaints.

Discussion

This systematic review showed that there is limited evidence for the effectiveness of e-Health on pain intensity in patients with back pain regardless of the extent to which the healthcare professional was involved in the online intervention and regardless of which control group was used. In patients with chronic pain, there is no evidence that remote e-Health without involvement of the healthcare providers is more effective than face-to-face interventions, but there is moderate evidence that it is more effective than no treatment. There is strong evidence that remote e-Health with minimal involvement of the healthcare provider is more effective than a control intervention in patients with chronic pain. For patients after total knee surgery, there appears to be a moderate effect of e-Health intervention with minimal involvement in the reduction of pain compared to no care. While for patients with osteoarthritis and knee pain there was no effect on the pain related outcome.

When we look at the between-group effect, this systematic review shows that there is no evidence between minimal healthcare provider involvement and no care in patients with back pain. There is, however, an indication that remote autonomous e-Health is more effective compared to face-to-face (usual care) treatments. Actually, according to another systematic review, autonomous e-Health interventions were not more effective than usual care in patients with low back pain [72]. According to the conflicting results in patients with low back pain, it may be that the interventions are insufficient tailored. Gains can be made when interventions are more adapted to individual needs and wishes. For low back pain, for example, the StarT-back screening tool is developed in which a treatment guideline is specified based on the risk profile [73]. The stepped care principle can also support personalized care, because the inclusion of giving autonomous home-based e-Health, blended care of face-to-face treatment requires careful consideration of whether a patient has motivation, access to digital services and the ability to process information [74]. The current systematic review looked into remote e-Health interventions. As it has been shown that a physiotherapist can positively influence treatment outcomes by providing positive feedback, giving answers on patient’s questions, and providing clear instructions for home practice [75], it could be useful to see with the studies included in the current review what the effect of just the e-Health application is, rather than the influence of the physiotherapist.

For patients with chronic pain, we found conflicting results regarding the effectiveness of remote e-Health interventions depending on the involvement of the healthcare provider and the control group. This study showed that when the online involvement of the healthcare provider increases, the evidence of effectiveness of remote e-Health compared to usual care also increases. Autonomous e-Health is more effective than no treatment, but not more effective than usual care unless there is minimal involvement of the healthcare provider. This is comparable to other studies in which they have shown that there is no evidence that autonomous remote e-Health has additional value next to usual care in patients with chronic pain [76], other than their cost-effectiveness [12,14,77,78]. Therefore, when applying e-Health in this population, it should be integrated in the intervention, also known as blended care, to provide effective treatment with lower costs [13,79].

The amount of effect of e-Health depends on usage and adherence [80]. Usage contains activity on the e-Health application, such as number of logins, minutes or activities completed [81]. Adherence is the extent to which a person uses the e-Health application as intended [81]. The studies included in the current review show contradictory evidence regarding usage, adherence and effect of the e-Health intervention [47,51,57,58,60,61]. For instance, some studies report low adherence rates but did not correct for this in the assessment of effectiveness [51,57,58]. In case of low adherence, i.e., the e-Health intervention was often not completely followed as intended, interpretation of the findings becomes more challenging [81].

In this systematic review, no significant effect was found in the population of osteoarthritis or patients with knee pain or knee surgery. Reasons why no effect was found is because of the relatively low number of studies included in this systematic review. Another explanation is that the pain outcome measure may not be the most obvious physical outcome measure for these populations. When looking at other outcome measures, for example physical activity, there is a significant long-term effect in favour of remote e-Health. At last, there might be a potential ceiling effect of the amount of therapy a patient can receive in which additional treatment would no longer lead to improved recovery [82]. In the case of pain intensity, evidence suggests that the content of the intervention is more important than the mode of delivery (i.e., in person or e-Health) [82]. Furthermore, there are big within-patient differences in the course of pain in patients with knee osteoarthritis [83], so with small sample sized and limited number of studies the interpretation of these results should be done with caution.

Limitations and strengths

A limitation is that even though this review only included remote e-Health interventions, there was still a large variety of intervention types (e.g., interactive websites, internet delivered programs and mobile applications) and pain related outcome measurements (VAS, NPRS, MDI, etc). Because of this, it was not possible to do a meta-analysis and interpretation of results must be done with caution. Another reason to be cautious is that the interventions in this review sometimes were given by a professional other than physiotherapists, for example by a physician-pain specialist [48], psychologists [42], nurse [60] or undefined therapists [38,40,45,51,62]. However, all included interventions can be delivered by physiotherapists as they are part of the modality physiotherapist have in the management of pain related complaints [23]. The final limitation of this study is that only within-group effect sizes are presented. It indicates the direction of the effectiveness of the intervention itself. Unfortunately, we were unable to abstract enough information from the articles to calculate the between-group effect sizes. Therefore, we extracted the p value of the between group effects per article which are presented in Table 2. A strength of this study is that all included articles were at least of moderate methodological quality, as assessed with the PEDro scale. All steps of this review were done with minimally two researchers independent from each other, which increases the reliability of the results. Additionally, both Pedro scale and Cochrane RoB tool were used so a more accurate conclusion can be drawn, as there are some slight discrepancies between the two tools.

Recommendations for future research and clinical practice

Based on the heterogeneity of interventions and outcome measures, we recommend to describe the used e-Health intervention carefully in future research so these interventions can be reproduced optimally. For example, by using the TIDieR checklist which aims to improve the completeness of reporting of interventions [84]. The TIDieR checklist includes 12 items, like brief name, why, what (materials), what (procedure), who provided, how, where, when and how much, tailoring, modifications and how well (planned) [84]. Additionally, future studies should look into the impact of patients’ experience of pain on daily functioning, and how e-Health can contribute to decrease the impact of pain.

While remote e-Health and usual care has no preference based on pain-outcome, literature shows in some cases that e-Health is more cost-effective than usual care [79,85]. The cost-effectiveness is in most studies still unclear and should be studied. It would be interesting for instance to determine the cost-effectiveness of e-Health interventions in chronic pain populations because of their social and economic burden [77,63].

When applying e-Health in the clinical practice, the amount of e-Health should be able to tailor specific needs of patients [86]. Some patients may have limited motivation to online consultation or other aspects of e-Health and patient preferences are an important aspect of evidence-based practice [87]. Another aspect of evidence-based practice is available evidence, to which this review contributes and shows that e-Health can be an effective way of reducing pain in some populations. However, this review is regarding autonomous e-Health interventions, and most physiotherapists will apply blended e-Health to ensure their full skillset, including hands-on therapy, can be used to help their patient with pain complaints [88–90].

Conclusion

There is limited evidence for the effectiveness of remote physiotherapeutic e-Health interventions to decrease pain intensity in patients with back pain. Autonomous e-Health is more effective than no treatment in patients with chronic pain, but not more effective than usual care unless there is minimal involvement of the healthcare provider. For patients with osteoarthritis, after total knee surgery and knee pain, there appears to be no effect of e-Health in the reduction of pain.

Disclosure statement

No potential conflict was reported by the author(s).

Appendix 1

 

Table A1. Search strategies.

Database	Search strategy Last performed: 25 February 2022	Results
Pubmed	("mobile health"[Title/Abstract] OR "web based"[Title/Abstract] OR web-based[Title/Abstract] OR "web"[Title/Abstract] OR "internet"[Title/Abstract] OR "computer"[Title/Abstract] OR mobile health[MeSH Terms] OR internet[MeSH Terms] OR ehealth[MeSH Terms] OR telehealth[MeSH Terms] OR "telehealth"[Title/Abstract] OR "telemedicine"[Title/Abstract] OR telemedicine[MeSH Terms] OR "ehealth"[Title/Abstract] OR "e health"[Title/Abstract] OR "mobile health"[Title/Abstract] OR "online"[Title/Abstract]) AND (musculoskeletal pain[MeSH Terms] OR "musculoskeletal"[Title/Abstract] OR musculoskeletal disease[MeSH Terms] OR physical therapy modalities[MeSH Terms] OR physical therap*[Title/Abstract] OR physiotherap*[Title/Abstract] OR exercise therapy[Title/Abstract] OR exercise therapy[MeSH Terms] OR "tension" OR "muscular" OR "myogenic" OR "myogenous" OR "myofascial") AND ("randomized controlled trial"[Publication Type] OR "controlled clinical trial"[Publication Type] OR "randomized"[Title/Abstract] OR "placebo"[Title/Abstract] OR "randomly"[Title/Abstract] OR "trial"[Title/Abstract] OR "groups"[Title/Abstract] OR randomized controlled trial[MeSH Terms] OR clinical trial[MeSH Terms]) NOT (("Animals"[Mesh] NOT ("Animals"[Mesh] AND "Humans"[Mesh]))	7510
EMBASE	telehealth:ab,ti OR ehealth:ab,ti OR internet:ab,ti AND ('physiotherapy’/exp OR physiotherapy OR 'musculoskeletal disease’/exp OR 'musculoskeletal disease’) AND (random*:ab,ti OR (clinical NEXT/1 trial*):de,ab,ti OR 'health care quality’/exp)	2023
Cochrane	("Musculoskeletal Disease" or "Musculoskeletal Pain" or Muscul* or "Physical Therapy":ti,ab,kw) and (eHealth or e-health or internet* or web* or tele* or online* or computer*:ti,ab,kw) Filter: trials	3098

Appendix 2.

List of excluded articles

175 were excluded because:

• the study design was not a RCT or had other issues (n = 41) [1–41],

• the patient population was not in line with our in- and exclusion criteria (n = 44) [42–85],

• the intervention was not in line with our in- and exclusion criteria (n = 53) [86–138],

• the outcome measures were not in line with our in- and exclusion criteria (n = 19) [139–157],

• other reasons (n = 18) [158–175].

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