Introduction

Rheumatoid arthritis (RA) is the most commonly diagnosed systemic inflammatory arthritis [1]. RA is a painful long-term condition which results in wide spread systemic inflammation, and joint damage [2]. Women, smokers, and those who have a family history of the disease are at highest risk of developing the condition [3]. RA affects 0·5–1·0% of adults, with 5–50 per 100,000 new cases annually in industrialised countries [4]. Uncontrolled active RA can result in erosive joint damage, increasing disability, poor quality of life, and other co-morbidities [5]. Patients with RA face a high disease burden including symptoms such as pain, stiffness, fatigue and decreased muscle strength which makes activities of daily living challenging. Furthermore, RA has been linked to psychological issues such as depression, helplessness and anxiety which further impacts on everyday life [6].

Treatment advances in RA have led to substantial improvements in patients’ physical and psychological outcomes, however, issues regarding fatigue, pain, reduced physical activity and quality of life still exist [7]. Poor outcomes are associated with non-adherence to medication, lack of knowledge about the condition and lack of support in coping and effective self-management [8]. Self-management is defined as the ability to manage symptoms, treatment and lifestyle changes which are associated with living with a chronic condition [3]. Evidence shows promoting self-management can contribute to better treatment and health outcomes through addressing these issues [9]. Engaging in self-management supports patients to take responsibility for improving their health by engaging in positive health behaviours such as physical activity, fatigue management and medication adherence [10].

There is potential for tele-health interventions (i.e. interventions delivered via digital technologies such as mobile phones, computers, text messaging) to provide cost effective, safe health care. The World Health Organisation (WHO) promotes the use of tele-health to provide clinical services [8]. The European League Against Rheumatism (EULAR) also recognise the benefits of tele-health for RA patients to enhance patient engagement and self-management approaches in rheumatic diseases [7]. Tele-health interventions can support self-management in RA by using technology to provide patients the knowledge, skills and support to manage their condition [7]. Research shows that patients in remission who are familiar with modern technology would welcome the possibility of managing their own condition by using online devices or mobile phones [11].

Although the use of tele-health interventions for RA is increasingly advocated, systematic reviews to date have only focussed on the quality and features of tele-health interventions available and their potential to be used within RA [11, 12]. These studies identified that tele-health interventions increase the emphasis engaging people with RA as active partners in their care and that a demand exists for technology that is accessible, simple to use and can help with the clinical management of the condition. Najm et al. [13] systematic review assessed the content and development of self-management tele-health interventions and endorsed that patients with RA are keen to engage with technology that supports self-management. A further systematic review by McDougall et al. [14] found tele-health interventions to be effective for the diagnosis and management of inflammatory rheumatic diseases but proposed that further studies were required to determine the best uses of tele-health for the management of these conditions. Knudsen et al. [15] qualitative study on tele-health in RA revealed the need for further insight into how tele-health interventions could be developed to increase patients to have an active role in disease control. To date no systematic review has assessed how effective telehealth interventions are at supporting self-management in patients with RA.

The purpose of this review is to answer the question: ‘Are tele-health interventions effective for supporting patients living with RA to self-manage their condition’? The review aims to evaluate a range of tele-health interventions and summarise the existing evidence base for their effectiveness. The aims include undertaking a systematic search and review of the literature in order to determine: (1) the extent to which tele-health interventions are effective in supporting patients to self-manage RA and (2) address implications for research and practice since no known systematic reviews of the effectiveness of tele-health interventions for supporting self-management in RA has previously been done. Examination of the literature for the usefulness and effectiveness of tele-health interventions in RA will provide insight into role that this technology could have to support self-management and improve patient outcomes.

Methods

Search strategy

The Preferred Reporting Items for Systematic Reviews & Meta-Analysis (PRISMA) statement and guidelines guided the conduct of this systematic review [16]. The following electronic bibliographic medical databases were systematically searched to identify trials of tele-health interventions supporting self-management in adults with RA: The Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MedLINE, Ovid Embase. The search terms were grouped into four concepts: (1) Rheumatoid Arthritis, (2) self-management, (3) tele-health interventions, and (4) study type. The search was limited to manuscripts published in the English language between 2014 and the present date to reflect developments in the tele-health industry and promote inclusion of contemporaneous studies. As an example, specific search terms undertaken in Ovid MedLINE on 3rd March 2020 is included (See supplementary File 1 for search strategy).

Inclusion/exclusion criteria

Randomised Controlled Trials (RCTs) were chosen as they measure the effectiveness of an intervention which addresses the research question posed in this review. Articles were included if participants were over the age of 18 and had RA, published in English, the intervention involved tele-heath technology to support self-management and incorporated outcomes of interest. (Table 1). An initial literature search conducted in March 2020 Identified 176 citations which were imported into Refworks and de-duplicated. After removing duplicates 98 articles were identified. First level screening was undertaken independently 98 titles/abstracts were screened and each article was assessed against the pre-set eligibility criteria which was ordered from 1 to 6. Inclusion criteria included language, type of study, participants, type of intervention outcomes and date. Seven studies were selected which met the inclusion criteria. Any discrepancies throughout the process were discussed with other authors (HH, CC) where there was any uncertainty, and the reasons for excluding the studies were recorded. The PRISMA flowchart Fig. 1 demonstrates this process in fuller detail.

Table 1 Inclusion/exclusion criteria
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Fig. 1
figure 1

Prisma flow diagram (Moher et al. 2009)

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Quality assessment

The quality appraisal tool chosen for assessing the quality of studies when undertaking a systematic review adhered to the PRISMA guidelines [16]. The Cochrane Risk of Bias tool was utilised to screen each study for bias [17]. This validated tool is recommended by Cochrane and was chosen for this review as it enables separate assessments of six specific domains of risk, including selection, performance, detection, attrition, and reporting bias. The tool was chosen as it enables a judgement to be made on whether a study has a high, low or unclear risk of bias. Risk of bias was undertaken independently with disagreements resolved by consensus with co-authors (HH, CC) (Table 2).

Table 2 Cochrane risk of bias tool [17]
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Data extraction and analysis

Data extraction were guided by the PRISMA statement [16] to ensure a systematic approach was adopted. Data were extracted using the Cochrane data collection form for intervention reviews: RCTs [2019]. The full-length papers were read, and data extracted, conflicts in data extraction were resolved by consensus with co-authors. Data collected included author, publication year, country of origin, details about the study population (diagnosis, mean age, disease activity, disease duration, and education level), the intervention, comparator, outcomes and study findings. Outcomes of interest related to self-management were grouped into disease control outcomes (clinical and physiological markers of disease control, health care utilisation and validated measures of symptoms) and self-management process outcomes (e.g. self-efficacy, medication adherence). To help determine the effectiveness of tele-health interventions in supporting patients with RA to self-manage their condition; the Template for Intervention Description and Replication (TiDier) checklist [17] was used as an additional data extraction tool. This checklist enabled extraction of further details of the key components of interventions, including identifying the type of technology used to support self-management, characteristics, including intensity, and duration, and underlying theoretical approaches the intervention. This information was then collated into tables to illustrate findings.

Data synthesis

Due to the extreme heterogeneity of the included studies in terms of participants, location, interventions and outcome measures a meta-analysis was statistically inappropriate and could not be performed. Therefore, a narrative synthesis was undertaken as interventions and associated outcomes were diverse.

Results

Outcome of the search

Figure 1 illustrates the flow of studies through the review process and outlines reasons for exclusion. Searches identified 176 potentially relevant articles which was reduced to 98 articles after removing duplicates by critiquing articles against inclusion criteria 1–4 (Table 2). Following review of title and abstracts 23 full text articles were retrieved. Of the 23 full text articles identified seven fully met the inclusion criteria 1–6 and were included in this systematic review.

Study characteristics and design

Table 3 shows details of each studies setting, participant demographics, interventions, comparator and key findings. The seven studies included in this systematic review dates of publication ranged from 2015 to 2020, six of the studies were undertaken from 2019 onwards reflecting the increasing use of tele-health interventions within healthcare. Each study involved a tele-health intervention and the comparison was usual clinical care. Three studies compared a web based tele-health intervention with usual care [18,19,20]. Allam et al. [18] trialled a self-management information website with specific groups accessing social support and/or gamification features. Salaffi et al. [20] & Zuidema et al. [19] incorporated self-monitoring tools to track symptoms within their self-management website. Three studies looked at telephone based self-management education sessions as an intervention following hospital discharge compared to usual care [21,22,23]. One study used a mobile phone text messaging (SMS) application to improve self-management by supporting medication adherence and self-monitoring of symptoms [24]. A wide range of outcomes to measure the effectiveness of interventions were included in the RCTs. The outcome measures were categorised as follows: disease activity and related symptoms, physical activity, knowledge and markers of self-care, medication adherence and health care resource use.

Table 3 Study characteristics & key findings
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Quality assessment and risk of bias

Results for the Cochrane Risk of Bias tool [17] for the included RCTs are reported in Table 2. Despite using random sequence generation participants were being selected from a group already identified to have access to either Internet or a mobile phone in several studies indicating a high risk of selection bias [18, 20, 24]. The risk of detection bias was unclear across all studies as it was not certain whether blinded outcome assessment had been performed. One study was assessed to have high risk of attrition bias as the data was incomplete [24]. All studies were unclear or at high risk of bias in most domains, common issues included small numbers of participants and a lack of blinding [20,21,22,23]. Other high risk of bias included participation bias as only those who had a telephone or mobile phone were recruited [20,21,22,23]. The studies were of low-to-moderate quality affecting overall validity, reliability and generalisability.

Synthesis of results

Intervention characteristics

Theories underpinning each intervention were identified and summarised in the TIDieR intervention Table 4. This mainly outlined cognitive-behavioural approaches, including self-efficacy theory [18, 23], empowerment theory, social support theory [18], theory of planned behaviour [19], dual process theory [19, 21, 23], and health belief theory [21]. A treat to target approach was mentioned as a key component of supporting self-management in two studies [20, 24]. Each intervention was delivered as planned with the overall aim of supporting self-management in individuals with RA.

Table 4 Tidier table – please see attachment 1
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Two web-based interventions [18, 19] and three telephone interventions were based on blended educational and behavioural theory [21,22,23]. Allam et al. [18] & Zuidema et al. [19] supported self-management through providing education and information on RA helping individuals to self-manage symptoms by targeting beliefs to affect behaviours. Allam et al. [18] incorporated a gamification feature, this approach based on self-determination theory aims to improve patient’s motivation and interaction with the website. A chat room option was available for some participants based on the theory that digital support networks may influence self-management capabilities. Behavioural theory appeared to underpin the telephone-based interventions, two were based on self-efficacy theory [22, 23] and one was based on the health belief model [21]. The main aim of these interventions was to empower patients to engage in positive self-management behaviours through planned telephone education sessions.

Two studies [20, 24] had a treat to target approach which aims to improve outcomes for patients with RA. According to NICE this is the preferred approach to provide clear direction on early treatment and tight disease control [25]. These interventions encouraged and empowered patients to be active partners in self managing their condition through online applications [24] and mobile phones [20]. These involved patients managing their condition through taking responsibility for self-monitoring of their symptoms.

Effect of tele-health interventions on outcomes

Disease Activity and related symptom measures

Zuidema et al. [19] web based self-management programme found no remarkable significant effects at six months on disease activity, pain or fatigue. The outcome measurement of the RAND-36 general health perception after twelve months showed a statistically significant effect (9.65. 95% CI 0.83–18.48, p + 0.03) for those who used the intervention more with a small effect size 0.02. Kuusalo et al. [24] self-monitoring SMS application found no statistically significant difference in disease activity DAS 28 at 6 months despite the level of remission being higher in the intervention group. Similar DAS 28 levels were achieved in both intervention and control groups during the first 6 months, the respective mean ± SD DAS 28 scores for the intervention and control groups were 1.92 ± 1.12 and 2.22 ± 1.11 at six months (p = 0.09); and 1.79 + 0.91 and 2.08 + 1.22 at 12 months (p = 0.28). Salaffi et al. [20] trialled a web based self-monitoring application and demonstrated several findings regarding disease activity which were statistically highly significant. A higher percentage in the intervention group achieved remission (38.1% vs 25% at 12 months p =  < 0.01). Time to achieve remission utilising the CDAI disease activity measurement (CDAI < 2.8) was significantly shorter in the intervention group with a median of 20 weeks versus a median over 36 weeks (p < 0.001). The patients in the intervention group also showed a greater improvement (p < 0.001) in terms of functional impairment (71.4% vs 35%) and lower radiographic progression of disease than the control group (intervention vs control group 1.47 vs 2.70; p = 0.009). Song et al. [21] telephone based self-management support measured disease activity and found there was no statistically significant difference between in disease activity at week 12 (p = 0.107) and week 24 (p = 0.096). Zhao & Chen [23] telephone based self-management education found no significant difference in the DAS 28 score at week 12 (p = 0.099) and week 24 (p = 0.096). In contrast Liu et al. [22] telephone based self-management education for patient post hospital discharge achieved statistically significant improvements in the intervention groups HAQ-DI gripping measurement at week 8 (1.17 vs 1.46 p = 0.01). This may be attributed to the focus on encouraging engagement in hand-joint exercises resulting in better clinical outcomes.

Medication adherence

Two studies looked at medication adherence as a primary outcome. Allam et al. [18] found the online self-management website did not impact medication adherence. Utilising the Prescription Medication Overuse Scale [26] a patient reported questionnaire to measure this outcome did not show any meaningful difference across the intervention groups (p = 0.056). Song et al. [21] telephone self-management education measured this outcome using the self-reported Medication Adherence compliance questionnaire [27] and found after the 12th week of the intervention medication adherence was significantly higher in the intervention group compared with the control group (p = 0.014). Similarly, the intervention group showed a significantly higher level of medication adherence than the control group at week 24 (p = 0.042). The effect size of the intervention on medication adherence was 0.58 (95% CI 0.12–1.03).

Physical activity

One study measured physical activity as a primary outcome [18]. Physical activity was measured using the validated patient reported Exercise Behaviours Scale [28]. The study noted an increase in physical activity at 16 weeks in the intervention group with access to additional features social support and gaming (p = 0.02). There were no statistically significant outcomes found for those using the website alone.

Health care utilisation

Allam et al. [18] evaluated the effect of a web based self-management programme on user’s health care utilisation. Utilising the validated self-reported Health Care Utilisation scale [29] to measure this primary outcome they found a significant decrease of visits to clinicians for patients accessing social support features (p = 0.01) and patients in accessing both social support features and gaming (p = 0.03). Kuusalo et al. [24] found their text-messaging to support self-monitoring application made no significant difference terms of health care utilisation. Measuring the outcome through the self-reported Utilisation of Health Care Resources scale [30] they found the number of unscheduled nurse’s visits was 0.56 + 0.80 in the intervention group and 0.56 + 0.65 in the control group (p = 0.56). In the intervention and control groups, the number of unscheduled physicians’ visits was 0.13 + 0.44 and 0.11 + 0.39 (p = 0.86) demonstrating no difference in the utilisation of healthcare resources. Liu et al. [22] measured hospital readmission scores as an outcome for the telephone self-management intervention. This did not show statistical significance although they did demonstrate clinically significant improvements as hospital readmission rates of the intervention group (4.5%) were lower than the control group (11.4%).

Knowledge and markers of self-care

Allam et al. [18] found that patients who had access to either social support sections or the gaming experience of their website intervention scored higher on the self-reported Empowerment scale [31] at 16 weeks (p = 0.03 & p = 0.05, respectively). They also found that the web intervention did not improve RA knowledge levels, measured by the validated self-reported Patient Knowledge Questionnaire in RA [32]. This outcome did not show any significant difference between control and intervention groups (p-0.06). Zhao & Chen [23] found that telephone self-management education following discharge from hospital had a significant effect on self-efficacy. At baseline there was no significant difference in self-efficacy (p = 0.072) between the intervention group and control group. However, in the 12th and 24th week after initiating the intervention the validated patient reported Rheumatoid Arthritis Self Efficacy (RASE) [33] score was statistically highly significant (p =  < 0.001).

Discussion

Five studies reported statistically significant outcomes to varying degrees [18, 20,21,22,23]. Zhao & Chen [20] and Liu et al. [22] found that tailored telephone self-management education following hospital discharge improved self-efficacy. This was measured using the patient reported outcome (PRO) Rheumatoid Arthritis Self Efficacy Score (RASE) [33]. Barlow et al. [34] endorse the RASE score to be a reliable and valid measure for people with arthritis and as a useful to evaluate self-management engagement. Liu et al. [22] assessed effectiveness over eight weeks and Zhao & Chen [23] up to 24 weeks. Further outcome measurements associated with improved self-efficacy such as medication adherence, physical activity and health care utilisation could have been considered in these studies to assess wider effects of the interventions. It would also have been of further interest to see how improvements in self-efficacy that these studies demonstrated impacted upon self-management behaviours over a longer period. Previous studies have shown self-efficacy to be a strong predictor of positive self-management behaviours among patients with other long-term conditions [35,36,37].

Song et al. [21] also found benefit from a tailored telephone delivered self-management support with significant improvement in medication adherence scores. This positive effect was self-reported through a validated questionnaire. Disease activity scores, however, were not improved although the effects of the intervention were only measured over the short-term 24 weeks. Longer-term effects of the intervention on other associated RA outcomes such as pain or fatigue scores could also have been considered. Chalfont et al. [38] supports that tele-health interventions focussed on improvement of patient self-efficacy and self-management may lead to improved health behaviours. These studies were implemented to support patients to self-manage on discharge from hospital which has been recognised as a vulnerable time for patients. Transitional discharge care describes self-management as a three-tiered simultaneous approach requiring knowledge, planning and ability to help patients manage their condition [39]. These findings suggest tele-health interventions may have an important role to play in supporting patients to self-manage following hospital discharge.

The tele-health interventions described in this review incorporated a range of self-management features. Findings demonstrate that tailoring interventions with multiple features or more intensive interventions may be associated with greater benefits [18, 20]. Vorderstrasse et al. [40] found that a tailored multiplatform website incorporating interactive components improved clinical, behavioural and psychosocial self-management outcomes in long-term conditions. Salaffi et al. [20] web-based intervention showed a highly significant improvement in disease activity supported by radiographic evidence over twelve months. Allam et al. [18] demonstrated that a web-based intervention incorporating gamification and social support features demonstrated significant improvements in physical activity and empowerment levels and a decrease in utilisation of health care resources. Johnson et al. [41] supports that gamification is an emerging strategy which can be beneficial to health and well-being. Further research with larger samples is required to derive meaningful conclusions on the effectiveness of these interventions to support self-management in RA patients.

Two included studies did not demonstrate any statistically significant differences on either the primary or secondary outcomes [19, 24]. Kuusalo et al. [24] tele-health intervention was based on the Treat to Target approach and promoted self-monitoring of RA symptoms [42]. Zuidema et al. [19] did not find any positive effects associated with a web-based tool to support self-management. This was surprising as the interventions were similar in both content and theory to other online self-management interventions which show positive effects in other chronic conditions [44,45,46]. Zuidema et al. [19] concluded that this could be due to the outcome measures chosen or perhaps the need to add a tailoring aspect to their intervention. Voncken et al. [47] supports that tailoring self-management interventions enhances patient engagement and the effectiveness of the intervention. Ammerlaan et al. [48] supports that tailoring an online RA self-management website by identifying individual goals and customising interventions improved usefulness and effectiveness.

In two studies was that participants had difficulty utilising intervention technology to undertake assessment of their own symptoms which affected their ability to participate [16, 21]. Both study authors acknowledged that they may need to redesign their interventions to be more user friendly and that patients should also be involved in the design process of future interventions. Education regarding how to use the technology was also limited and participants may have benefited from further training. Tuckson et al. [43] notes the importance of designing tele-health technology to be user friendly along with education to promote ease of access.

Overall, this review found the evidence of the effectiveness of tele-health interventions to support self-management to be inconclusive but promising. This is like the findings reported by other reviews of tele-health within other long-term conditions [49,50,51]. A consistent conclusion reflected in the findings of this review is that utilising tele-health interventions was not associated with worse outcomes or harm to any patient. Taking this in context it appears that whilst not consistently superior to usual care, tele-health interventions provide a safe alternative mode of delivery for supporting individuals to manage their RA. Whilst there are some promising indications of benefits that may be associated with effectiveness of the interventions it may be that sample sizes were too small to detect differences, or tools used to measure outcomes were unable to detect significant differences between groups.

Limitations and future research

There were several limitations identified in this narrative review. Searches were limited to three databases due to time constraints and no access to an information specialist. Keywords could have been further refined to enable a more comprehensive literature search. A further limitation is that the search was also limited to studies published in English and did not look for non-English publications or unpublished literature, so it is possible that relevant studies were missed. The degree of bias within this review by only including studies published in English and the likely associated publication bias with including only published papers is acknowledged. However, the tools used in the review were rigorous and are of a high quality for both undertaking the search and extracting including the Cochrane risk of bias tool, Cochrane data extraction tool and TidIER Checklist. The methodological assessment tools used in the review are in line with the recommendations of the PRISMA statement [16].

The search identified that the studies were heterogeneous in terms of the intervention approaches, outcomes and associated contexts. The heterogeneity resulted in a further limitation as the small number of articles and associated diversity prevented the study findings being subject to a meta-analysis. The quality appraisal identified that the studies were largely deemed to be of low to moderate quality and at a high risk of bias. The sample sizes, outcome measures, short-term evaluation of interventions and locations of the studies raises further issues around the validity, reliability and generalisability of the findings from this review. Despite these limitations this review provides valuable insight into the effectiveness of tele-health interventions to support self-management in RA.

Further high-quality research is required to assess the long-term effectiveness of tele-health interventions and future trials undertaken should ensure objective outcomes are measured alongside PRO or subjective measures to increase the validity and reliability of results. Kilic et al. [52] supports that PROs are helpful in providing information to accompany clinical investigations and can be used to assist with guiding patient care, such as determining whether a clinic visit, or treatment changes are necessary. This review has highlighted for the purpose of assessing the effectiveness of tele-health interventions to support self-management that it would also be helpful if objective measures such as blood tests and radiographic evaluation were studied alongside PRO measures to support validity of findings.

The design of tele-health interventions was highlighted as an important aspect of enhancing effectiveness and patient engagement. Further research will determine the most effective behavioural and educational theories on which to base interventions. Multi-platforms and tailoring of tele-health interventions were more likely to demonstrate efficacy and interactive aspects such as social support and gamification require further exploration [18, 20]. There is growing evidence that tailoring tele-health interventions is a more successful approach and this will be an important issue to consider for future research [53]. This review also suggests that patients should be involved in the designing of tele-health interventions to promote engagement and usability. Clinicians must ensure that patients receive enough education to improve tele-health intervention literacy. Salisbury et al. [54] supports that the decision to utilise tele-health intervention to support self-management requires consensus between patient and clinicians. Further studies will help determine the wider impact of tele-health interventions on health care utilisation and associated potential benefits such as improved healthcare access and cost effectiveness.

Conclusion

This review has highlighted that the published literature regarding the effectiveness of tele-health intervention to support self-management in RA is extremely heterogeneous. The existing evidence is limited and has not yet proven the effectiveness of tele-health interventions although there are indicators regarding its usefulness to support self-management. There are signs within this review that positive self-management outcomes are linked with tele-health interventions that are well designed, tailored and multi-faceted. Going forward tele-health interventions for individuals with RA should be patient centred, building on specific self-management theory and ensuring adequate resources are invested in education and training for users. Technologically the benefit of adding dynamic elements such as gamification to enhance interventions requires further consideration, however, simple approaches such as basic telephone interventions to support self-management have also shown to be of value in this review. Larger scale RCTs of tele-health interventions to support RA self-management are now required along with the exploration of objective validated outcomes and measurement of long-term effects. Determining the extent to which the benefits of tele-health can be harnessed to support self-management in RA will be of utmost importance as virtual care becomes increasingly utilised and especially so in the health culture of the current pandemic.