Preparing to deliver a stepped wedge cluster-randomised trial to test the effectiveness of daily symptom tracking integrated into electronic health records for managing rheumatoid arthritis: a mixed- methods feasibility trial

doi:10.21203/rs.3.rs-4712251/v1

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Research Article

Preparing to deliver a stepped wedge cluster-randomised trial to test the effectiveness of daily symptom tracking integrated into electronic health records for managing rheumatoid arthritis: a mixed- methods feasibility trial

https://doi.org/10.21203/rs.3.rs-4712251/v1

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Background:

We sought to assess the feasibility of a stepped-wedge cluster-randomised trial testing the effectiveness of a complex mHealth intervention called REMORA: a co-designed smartphone app enabling daily symptom tracking integrated into electronic health records (EHR) for people with rheumatoid arthritis (RA).

Methods:

We recruited patients aged ≥ 18 years with RA or inflammatory arthritis who consented to track their symptoms for up to six months using REMORA, from two outpatient departments. We analysed quantitative app and EHR data descriptively. Qualitative data from interviews and clinic observations was analysed thematically. We assessed four feasibility domains: recruitment and consent (target: 15 patients per site), intervention uptake (≥ 70% of recruited participants completed on-boarding), intervention adherence (> 50% daily symptom reports provided) and measuring disease activity as the primary outcome (scores available for ≥ 80% of people with a follow-up clinic visit).

Results:

Of 52 people consented, 32 (62%) completed on-boarding. Participants provided symptom data on 62% of study days. Among 48 people who had ≥ 1 follow-up appointment, at least one disease activity scored was obtained for 46 (96%) of them. Factors related to intervention uptake formed the biggest threat to trial feasibility, including lack of clarity of communication and guidance, access to technology, and personal challenges (e.g., being busy or unwell).

Conclusion:

We found that delivering a stepped-wedge cluster-randomised trial to test the effectiveness of REMORA would be feasible. This trial will contribute to the much-needed evidence base for the impact of integrated symptom tracking on care delivery and patient outcomes, including decision-making, patient experience, disease activity, and symptom burden.

Trial registration:

This feasibility trial was registered at https://www.isrctn.com/ on 23-Jan-2023 (ISRCTN21226438).

Rheumatoid arthritis

Feasibility studies

Patient-generated health data

Smartphone applications

mHealth

symptoms

Rheumatoid arthritis (RA) is an exemplar for long-term conditions that may benefit from remote monitoring, with data integrated into health information systems and clinical workflows ^[1]. People living with RA, a common immune-mediated inflammatory disease, typically receive outpatient rheumatology care 1–4 times a year, with fluctuations in well-being and symptoms, such as pain and fatigue, between visits^[2–4]. Recall and descriptions of these fluctuations are poor, hampering optimal clinical and self-management ^{[2, 4, 5]}. Developments in mobile technology and health apps have revolutionised possibilities for clinical- and self-management of long-term conditions, including rheumatic diseases (such as RA), by minimising the reliance on patient recall through frequent symptom tracking and therefore providing a clearer and more accurate picture of changing symptoms through time ^{[2, 6]}.

Up to 86% of people with rheumatic diseases are willing to use symptom monitoring apps to improve their disease management^{[7, 8]}, and healthcare professionals responsible for treating RA perceive a benefit from gaining insight into the day-to-day lived experience of their patients^[9]. Evidence from a range of long-term conditions has indicated that the collection of patient-generated health data and its integration into electronic health records (EHRs) could improve shared decision-making and patients’ satisfaction and self-management, and decrease anxiety ^{[4, 8, 10–12]}. Further potential benefits may include more efficient utilisation of healthcare services, benefiting not just patients, but service providers and the wider economy^[13].

Despite growing interest in integrating these complex remote monitoring interventions into clinical systems and processes, evidence of their impact on services and outcomes remains scarce ^[14]. Previous studies were not randomised, small (e.g., single centre studies), recruited a highly selected sample, collected symptoms infrequently (weekly/monthly), used low-tech interventions (e.g., SMS), and/or did not integrate the tracked symptom data in EHRs ^[14–18].

We previously demonstrated proof-of-concept of the REmote MOnitoring of Rheumatoid Arthritis (REMORA) system, a complex mHealth intervention that enables people living with RA to track their symptoms daily, integrate REMORA data into the EHR and share these with their rheumatology team^[6]. Having shown that both patients and clinicians were positive about the REMORA system, we wished to scale up the use of REMORA and study its impact on clinical outcomes using a multi-centre stepped wedge cluster randomised trial (herein “REMORA trial”). The trial will aim to evaluate the effectiveness of REMORA on care delivery and patient outcomes, such as disease activity, decision-making, patient experience, and other patient priorities, such as pain and fatigue.

Prior to undertaking the REMORA trial, we needed to gain a comprehensive understanding of the likely feasibility of, and potential barriers to, conduct of such a trial, to understand whether it was feasible to proceed, and to optimise recruitment and participation. However, traditional feasibility performance measures (such as rates of recruitment and intervention uptake) may miss certain nuances in the participants’ experiences and challenges necessary to overcome for a successful trial. Therefore, in order to better understand the feasibility of our proposed trial, we conducted an integrated mixed-method feasibility trial^{[19, 20]} to elucidate areas of agreement and dissonance between traditional feasibility performance measures such as rates of recruitment and intervention uptake, with the lived experience of the individuals who supported, participated or declined participation in the feasibility study, via interviews and clinical observations.

We reported this study in accordance with the CONSORT 2010 statement extended for pilot and feasibility trials^[21] (Table S1 in Supplementary materials).

Context: a planned cluster-randomised stepped wedge trial to evaluate the effectiveness of integrated symptom tracking (the REMORA trial)

The REMORA trial will be conducted within rheumatology outpatient care in England, United Kingdom (UK), using randomisation at site-level (i.e., cluster randomisation). Site randomisation will determine the time at which recruited participants at sites are allocated to standard-of-care or integrated symptom tracking, i.e., patients recruited to trial following the switch over from usual care will start using the REMORA system (see intervention description below). Follow-up will last 12 months from date of recruitment for each participant, with clinical evaluation based on routine visits requested by clinical care teams, rather than additional research visits. The primary outcome measure will be disease activity as recorded by clinical care teams; disease activity score for 28 joints (DAS-28) for in person appointments and clinical disease activity index (CDAI) for remote appointments^[22]. Secondary outcomes, collected via web surveys, include patient reported symptoms (e.g. pain, fatigue), work productivity and disease activity (e.g. joint counts, patient global). A mixed-methods process evaluation will determine the effectiveness, and underlying mechanisms, of the REMORA system.

Feasibility setting, participants, intervention, and procedures

Design, setting and ethical approval

Our mixed-methods feasibility trial used a convergent approach (i.e., concurrent data collection and analysis) with some explanatory sequential elements (i.e., quantitative data analysis guided some of the qualitative data collection and analysis)^[23]. The study took place in two rheumatology outpatient departments in Greater Manchester, United Kingdom. The study received ethical approval from the UK Health Research Authority, South Central - Berkshire B Research Ethics Committee (ref: 22/SC/0103).

Participants

We first recruited and consented rheumatology healthcare professionals responsible for patient care to take part in the study. This included consent for reviewing the remote monitoring data in consultations for consented patients with an optional interview and/or clinic observations. Site recruitment teams then identified potential patient participants under the care of consented healthcare professionals. Eligible patients were adults (i.e. ≥18 years of age) with confirmed or suspected RA or inflammatory arthritis, and an Android or iOS smartphone with daily internet access. They were asked to report daily, weekly, and monthly symptoms tracking using REMORA. No restrictions were placed on the level of disease activity experienced by patients at the point of consent. As REMORA is currently only available in English, we excluded patients who could not speak and understand English and had no support from someone who did.

Potential participants were given a participant information sheet and time to review the study information and ask questions, before providing informed consent to download and use the REMORA app. We also asked consent to be contacted for an additional interview and/or clinic observation. Individuals who declined participation in the feasibility trial were asked consent for being interviewed about their reason(s) for not wanting to take part. Written or verbal consent before interviews and/or observations was obtained.

Intervention

The REMORA system is a complex mobile health (mHealth) intervention comprising a co-designed smartphone app that enables people living with RA to track their symptoms daily. The app is linked to regional data infrastructure for integrating symptom data into participating local hospitals’ EHR systems; this facilitates review of the data at forthcoming outpatient consultations. REMORA has been co-designed with members of our patient and public involvement and engagement (PPIE) group, who have been instrumental in developing and refining the app and supporting materials. The REMORA system was well received by patients and healthcare professionals in an initial proof-of-value study at a single site and showed potential to enhance clinical encounters ^[24].

REMORA users provided daily reports for the seven symptoms from the Rheumatoid Arthritis Impact of Disease (RAID) score[25] on 0–10 visual analogue scales (VAS): pain, function, fatigue, sleep, physical well-being, emotional well-being and coping; they also reported their duration of morning stiffness on a 7-point ordinal scale. They were asked to submit weekly and monthly questionnaires to collect information on domains such as self-reported flares, work productivity^[26], and disability^[27].

This patient-generated symptom data was automatically sent daily to a secure server managed by the regional Integrated Care Board (i.e., the body responsible for regional healthcare service delivery). Data was then presented as graphical visualisations via a bespoke, interactive REMORA dashboard available within their local EHR system using single sign-on, meaning a clinician logged onto a particular patient’s record would be able to immediately access that patient’s REMORA data without further sign-on or searching for the specific patient. Rheumatology teams received training for accessing and using the dashboard to support them reviewing the data presented, discussing this with the patient during their clinic visit, and making treatment decisions accordingly. Symptom data was not routinely reviewed in between visits and patients were advised to use normal procedures for seeking help in the event of flare or difficulty. Data was only visible to healthcare professionals who had undergone training and been provided with access. Screenshots of the smartphone app (Figure S1) and the interactive dashboard (Figure S2) are shown in Supplementary materials.

Trial procedures under evaluation

We evaluated the feasibility of the REMORA trial’s design and processes across four feasibility domains: (1) Recruitment and consent, (2) Intervention uptake: “On-boarding”, (3) Intervention adherence: “Completeness of symptom tracking”, and (4) Outcome measurement. We specified a-priori criteria to assess each domain, reflecting thresholds we considered meaningful for the conduct of the REMORA trial (See Table 1).

Table 1

– A priori assessment criteria used to evaluate the feasibility domains in this feasibility study
Domain	Assessment criteria	Trial feasible	Trial feasible with adjustments	Trial not feasible
(1) Recruitment and consent	Number of patients per site consented to symptom tracking	≥ 15	11–14	≤ 10 per site
(2) Intervention uptake: “On-boarding”	Proportion of consented participants who successfully downloaded and registered with the app and completed at least one symptom report	≥ 70%	50–69%	< 50%
(3) Intervention adherence: “Completeness of symptom tracking”	Proportion of study days on which participants recorded at least one symptom out of all possible days	> 50%	25–50%	< 25%
(4) Outcome measurement	Proportion of participants who had ≥ 1 follow-up visit and at least one disease activity score available	≥ 80%	50–79%	< 50%

Recruitment and consent

Sites were asked to recruit as many members as possible of the rheumatology teams primarily responsible for making treatment decisions, to maximise available patient participants and for ease of follow up by teams. Sites were then each asked to recruit up to 30 eligible patients over a period of 13 weeks, with a minimum target of 5 per month (i.e. the 15 participants per site required to meet the “Trial feasible” threshold). This sample size allowed us to estimate a participant follow-up rate of 80% to within a 95% confidence interval of +/-14%.

Intervention uptake: “On-boarding”

Following consent, trial participants received a welcome email with instructions to download, register with and use the study app (Fig. 1). Within the welcome email participants were also provided a link to complete a baseline web survey, which provided some additional demographic data and secondary outcome measures, including work productivity, disability and resource use. Participants were considered ‘on-boarded’ if they appeared in registration logs obtained from the app data as having successfully submitted their unique study identifier and activation code, completed permissions to link and create/use the NHS login, and submitted at least one symptom report. An active on-boarding window sought to encourage on-boarding within 18 days of the initial email being sent. Non-registration reminders were sent 3, 7 and 14 days after the initial invite, as required, via email. ‘Non-tracking’ reminders were sent to participants who had registered in the app successfully but had not recorded any symptoms within 3, 7 and 14 days of registration. Participants who had not completed on-boarding by day 18 were considered to have failed on-boarding. Registration after day 18 was possible but was not actively encouraged by further reminders.

Intervention adherence: “Completeness of symptom tracking”

An in-app notification prompted participants each day to complete their daily (at 6.30pm, confirmed as a feasible time in our proof-of-concept study^[6], weekly (at 3.30pm) and monthly (at 12.00pm) questionnaires. Participants were followed for up to 6 months. Adherence to symptom tracking was calculated based only on the completion of the daily questionnaires. Additional reminders to symptom track were sent on up to two occasions if people had not completed at least one symptom per day on at least 50% of days between tracking commencement and days 7 and 14. In addition to this formal domain criteria, we explored adherence against a-priori defined adherence clusters of low (< 25% days), moderate (25–60% days) and high adherence(> 60% days).

Outcome measurement

Site staff extracted data from EHRs at baseline and for all follow-up visits that occurred in the follow-up window (up to 6 months). Data included demographics at baseline, clinical history, and disease activity. Collection of disease activity by clinicians during the clinical consultation is the primary outcome of the main trial, via the DAS28 for in-person appointments or CDAI for remote appointments, and thus successful completion of these metrics was the focus for our outcome measurement evaluation.

Data collection

Demographics

Baseline demographic data were primarily collected from the EHR by site staff using a secure study-specific electronic data capture tool hosted at The University of Manchester. Extracted data included participants’ sex, date of birth (to calculate age), ethnicity, smoking status, Body Mass Index, recorded diagnosis (RA, suspected RA, inflammatory arthritis or suspected inflammatory arthritis) and date of diagnosis (to calculate disease duration). Missing data for ethnicity and smoking status were replaced with data from a self-report web survey completed by participants at baseline.

Feasibility evaluation

The data for evaluating the feasibility of our proposed trial design came from a range of sources. Briefly, quantitative data was obtained from sites, the app or via EHR extractions pertaining to screening and recruitment, app registration and daily symptom reports, and availability of disease activity scores. Healthcare professionals were asked to record how REMORA data were used within their consultation and whether they found it useful.

Interviews were conducted among 1) patients who declined symptom tracking participation (“decliners”), 2) those who consented to study participation, but did not register with the app by day 18 (“non-registered participants”), 3) those who registered with the app, stratified based on their level of adherence (see below Quantitative data analysis for definitions), and 4) healthcare professionals. A series of professional-patient dyads were also observed during consultations, where both parties had provided optional consent for their consultations to be observed, up to a maximum of three consultations per professional. Study team logs were maintained throughout the trial to detail field notes from interviews/observations and summaries of contact (phone/email) between the study team and participants or site staff.

Table 2 describes further information on the data sources, and information regarding to which feasibility domain evaluations they contributed.

Table 2

Data sources used to explore the feasibility domains: (1) Recruitment and consent, (2) Intervention Uptake: “On-boarding”, (3) Intervention Adherence: “Completeness of symptom tracking”, (4) Outcome measurement
Data source	Description	Feasibility Domains
Data source	Description	(1)	(2)	(3)	(4)
Screening/recruitment logs	Screening and recruitment logs were provided by sites, detailing the number and outcome of patient approaches, and reasons for ineligibility or declining participation.	X
Quantitative data on registration rates (via app records)	Registration logs (including study identifier and date of registration) were obtained from the app records and used to calculate the rate of registration.		X
Quantitative data on adherence rates (via app records)	App records provided information about all symptoms reported by participants each day, linked to their study identifier. Participants were considered to have “engaged” with the app on a day on which they provided at least one of the 8 daily symptom reports (see Intervention).			X
Quantitative data on availability of disease activity at baseline/follow-up (via EHR extraction)	A comprehensive selection of data was extracted from the participants’ EHRs and inputted into a secure study-specific database. Of interest here are disease activity data only.				X
Healthcare professional reported symptom data use (questionnaire)	A brief survey recorded healthcare professionals use of the REMORA data within their consultation, including information about when they looked at the data, whether they looked at the data with the patient and how useful the data were. A free-text box was provided for any additional comments regarding how data were used during the consultation.			X
Interviews with patients or clinicians	One-to-one semi-structured interviews (duration 7–35 minutes) took place by telephone, face-to-face, or using video-conferencing software, depending on participant preference. Discussion topics included the use of technology for health monitoring in general and, where applicable: 1. perceptions of the REMORA system 2. the impact of REMORA system and tracked symptom data on the clinical consultation and decision making 3. reasons for their (lack of) adherence with symptom tracking 4. reasons for declining participation in the study, or for not completing on-boarding	X	X	X
Observations	Observations (duration 12 to 36 minutes) of consultations..			X	X
Study team logs	Study team logs comprised field notes from interviews, observations and summaries of contact/correspondence with participants or site staff via phone or email.	X	X	X

Data Analysis

Quantitative data analysis

We evaluated study performance descriptively against the assessment criteria presented in Table 1, by determining rates of recruitment, on-boarding, adherence (based on the proportion of possible days on which participants tracked their symptoms) and outcome availability. We also explored adherence in terms of membership of one of the a-priori defined adherence clusters (see Intervention adherence: “Completeness of symptom tracking”), though this did not formally contribute to our feasibility criteria.

1) and 2)

Qualitative data analysis

Interviews and observations were audio-recorded, transcribed, deidentified and thematically analysed using ^[28] NVivo 12 Plus software. The Theoretical Domains Framework guided our analysis of data from patients who tracked their symptoms by providing a lens for considering how the intervention influenced participants’ behaviours ^{[29, 30]}. For analysing the healthcare professional interviews and clinic observations, the Three Talk model of shared decision-making served as a guide^[31] to examine the collaborative decision-making process between patients and professionals. Preliminary codes were systematically collated into potential themes using a constant comparative method, with review sessions to ensure that data extracts effectively represented analytic themes and to identify further subthemes where these emerged from the data.

Study team logs were reviewed to complement the quantitative and qualitative data analyses by providing additional insight and contextual information.

Mixed-methods interpretation

Following guidance by Aschbrenner et al^[23], we created “joint displays” to bring together findings from the quantitative and qualitative analyses and interpret them together using an integrative approach.

Recruitment and consent

Across both sites, a total of 130 patients were screened for participation, of whom 78 (60%) were excluded as they either declined participation (n = 38, 49%) or were otherwise ineligible (n = 40, 51%). The most common reason for exclusion was contact lost during consent or inability to be contacted (n = 21, 53%), followed by a lack of access to appropriate technology (smartphone/email; n = 10, 25%); other reasons are shown in Fig. 2. In total 52 people were consented (20 at site 1 and 32 at site 2), which returned a “Trial feasible” evaluation for Domain (1).

Intervention uptake: “On-boarding”

Among the 52 people recruited and consented (site 1: 32, site 2: 20), two (4%) withdrew before commencing on-boarding and 18 (36%) did not complete on-boarding, with the remaining 32 (62%) successfully completing on-boarding (see Table 3). Thus, we returned a “Trial feasible with adjustments” evaluation for Domain (2). To support on-boarding we sent participants a total of 76 email reminders. Of those 76, 66 reminders were sent for non-registration to 37 participants, of whom 17 (46%) then completed registration. Table S1 in the supplementary materials shows no substantial differences between those who were consented and those who on-boarded, though more people of white ethnicity appeared in the on-boarded group (94% (95%CI: 79–99) vs 87% (74–94), respectively).

Intervention adherence: “Completeness of symptom tracking”

Among the 32 on-boarded participants, rates of adherence showed that participants provided symptom data on 62% (2384/3771) of possible days. 20 (62%) participants achieved high adherence (see Table 3), while the low and moderate groups each comprised six participants (19%). Nine reminders were sent to seven participants for having less than 50% of days since tracking, with two of them having a final completion rate of 78% and 97%. We observed few differences between low vs high engagers, though low engagers (n = 6) were younger (median (IQR) years: 42 (35–55)) than the high engagers (n = 20; 61 (51–65) and had a shorter disease duration (years: 1 (0–6) vs 3 (1–11) (Table S2 in Supplementary materials). Given that adherence rates exceeded the > 50% threshold, we achieved a “Trial feasible” evaluation for Domain (3).

Table 3

Comparisons of demographic characteristics for a) those who were recruited vs. those who on-boarded and b) low vs high adherers1
		Recruited vs on-boarded		Low vs High adherers¹
		Recruited participants (n = 52)	On-boarded participants (n = 32)	Low adherers (n = 6)	High adherers (n = 20)
Female (n, (%))		36 (69%)	23 (72%)	6 (100%)	13 (65.0%)
Age (median (IQR))		58 (48–65)	57 (48–63)	42 (35–55)	61 (51–65)
White ethnicity (n, (%))		45 (87%)	30 (94%)	4 (67%)	20 (100%)
Smoking² (n, (%))	Current	11 (21%)	5 (16%)	1 (17%)	3 (15%)
	Former	22 (42%)	13 (41%)	2 (33%)	9 (45%)
	Never	18 (34%)	13 (41%)	3 (50%)	8 (40%)
BMI³ (n, (%))	Normal	17 (33%)	11 (34%)	2 (33%)	5 (25%)
	Over	19 (36%)	10 (32%)	1 (17%)	9 (45%)
	Obese	16 (31%)	11 (34%)	3 (50%)	6 (30%)
Diagnosis⁴ (n, (%))	RA	50 (96%)	31 (97%)	6 (100%)	20 (100%)
	Inflammatory arthritis	1 (2%)	0	0	0
	Suspected RA	1 (2%)	1 (3%)	0	0
Disease duration (median (IQR))		3 (0–10)	4 (0–11)	1 (0–6)	3 (1–11)
¹ Low adherers– symptoms reported on < 25% days; High adherers - symptoms reported on > 60% days. ²smoking status missing from medical record for one on-boarded participant ³ No one is underweight, so category omitted ⁴ No one suspected IA, so category omitted

Outcome measurement

Figure 3 shows that, among 50 consented participants who did not withdraw before on-boarding, 49 were eligible to have a follow-up visit during the study window as one participant withdrew after nine days in the study, prior to follow-up. Of those 49, 48 (98%) had ≥ 1 appointment, of whom 46 (96%) had ≥ 1 disease activity score which had been completed by a clinician in the consultation. Compared to the upper threshold of ≥ 80%, we therefore achieved a “Trial feasible” evaluation for Domain (4).

Overall evaluation of feasibility domains

In summary, when comparing our quantitative findings to the assessment criteria outlined in Table 1, we confirmed that the existing design of the REMORA trial was feasible for domains 1, 3 and 4. However, we found on-boarding rates were suboptimal, suggesting that domain 2 (intervention uptake) required adjustments to enhance its feasibility.

Challenges to intervention uptake/on-boarding

Analysis of our qualitative data sources identified five challenges which differentially impacted on all four of the feasibility domains and should therefore be considered for modification in the main trial. The challenges which affected domain 2 will be discussed in detail below, with all other challenges being summarised and illustrated with quotes (where available) in Table 4.

Table 4

Description of challenges and the feasibility domains they affected; (1) Recruitment and consent, (2) On-boarding, (3) Symptom tracking and review, (4) Outcome measurement
Challenge	Sub-themes	Description and illustrative quotes from qualitative data sources	Affected feasibility domains
Challenge	Sub-themes		(1)	(2)	(3)	(4)
Clarity of communication and guidance	Clarity of app use at recruitment	Without access to an app demonstration account, staff reported they had struggled to verbally explain the functionality of the REMORA app and what patients would be required to do.	X	X
	Privacy statement	The lack of a comprehensive privacy statement may explain why some people who declined to take part raised concerns about privacy and data security. “Yeah, but still, I am not sure I am not too keen about who uses the data and then if it lost that’s stressful.” (PD03) Although some of those who consented but did not on-board shared these concerns, others stated they had no concerns about this or asked for and got reassurance about data privacy. “Well, yeah, I mean, obviously I wouldn’t want anybody knowing all my information, but I understand, I’m sure it would be, you know, fine, to be honest.” (PNR03) “I think it was [name of clinician] that phoned me and initially went through it all, I think I bombarded her with questions really about it all, because we were so up on data protection and everything. So I was a bit concerned, but she just allayed any of my fears really.’ (PT19)
	Clarity of patient facing instructions	Some participants reported problems with patients downloading or using the app due to a lack of communication or unclear/complex guidance. “[patient] hadn’t downloaded the app. She said that she couldn’t understand the instructions. She still wanted to take part in the study, contact number for research team given for advice.” – (HPQ01) “No, the only issue I had using your app was the fact that they didn’t send me the link. A colleague emailed me saying, I’m sorry we didn’t send you the link. So that’s why I couldn’t get on it. I didn’t know what it was looking for.” (PNR04) “Yeah. I was told the study would be online through an app or something, but when I got the email…see if I can get a copy. When I click on the pages, it had three attachments and I clicked on one of them and it just came up with a blank screen.” (PNR05) A number of additional app features, such as the diary function, were not appropriately described within instructions. “Yeah, and I went through the instructions when I first set it up. Unless I’ve totally missed something, I don’t know, I’ve never been highlighted or signposted towards a diary, but that could be me, I could’ve missed that. I don’t remember anything in the instructions but maybe there was.” (PT15)
Accessibility	IT requirements for taking part	IT requirements meant some people were unable to participate. A lack of access to a smartphone device, accounting for just over one-fifth (22%) of those who were ineligible to participate. “I had to get a new phone because it didn’t have the right issue [version] on. It was obviously a little bit older because I hadn’t gone in and updated it on my contract thing.” (PT08) “If I didn't need the pin on my phone. It sounds silly doesn’t it, but it's just something [..] that I don't want to do. So it's a shame, because like I say, the study itself, everything else, I don’t have an issue with at all.” (PNR09) Many participants indicated that they only checked emails irregularly or not all, meaning that communication from the research team were often missed and therefore not actioned. “Yeah. It’s like I don’t use the computer every day so it’s not like I’d re-checked or was aware this was happening. Yeah, like I say, because I wasn’t using the computer every day it’s not that I didn’t want to but never checked the emails.” (PNR05) “I don’t check the emails regularly.” (PNR08)	X	X
	Personal barriers	Personal barriers to participation included: 1. RA-related challenges (e.g. high levels of pain, dexterity, forgetfulness or brain fog) 2. non-RA related challenges (e.g. dyslexia, a lack of confidence with using technology) 3. difficulty integrating the study into everyday life (i.e. when people were too busy, ill, or otherwise committed) at the time of study 4. motivations and perceived benefits, particularly regarding the use of the collected data in clinic, or of health apps more generally. “It’s hard for some people, like me, I say, me with my bad fingers. I cannot possibly do such things I will not be able to sustain what I start because of my hands.” (PD03) “Because I haven’t been well and my brain has been fog. When I got over that bout, I’m still quite poorly with getting over the flu. I’ve had a bad run of ill health at the moment. I would be willing to download the app and do everything once my medication is …” (PNR01) “Was unable to enter data - issues with her phone and not being tech savvy. Did seek support but unfortunately did not rectify the issues.” (HPQ02) “Maybe, yeah. I’m just that busy that I knew that something was missing but I just didn’t follow it up right away because I was mad busy elsewhere.” (PNR11) “[Patient is] Happy to track but found the weekly one not relevant & was busy.” (HPQ03) “To be honest, I’ve not really searched for apps that would help me. The trouble with apps is, there are so many, you don’t know if you’ve got a good one.” (PNR10)
	Provision of support	It was highlighted that those who were more nervous about their ability to participate would have benefitted from greater support available during on-boarding and commencing data collection. Some people had sought additional support from outside of the study team (e.g. friends/family, in clinic). “… patients really want to participate in these trials and want to join in, but it’s just way too complicated. Unless… we can have that time to say, ‘alright, well if you’re happy we’ll do it with you there and then’ and get them set up and running.” (HPI01) “…and on top of that I didn’t know how to use these devices and would need a lot of help.” (PD01) “I didn't realise I had to put a pin code in my phone to stop anybody getting into my phone. It was my daughter that sorted it. I told you I'm not very technical.” (PT03)
Symptom data collection	Data collected (type and frequency)	Patient participants queried the type and frequency of data collected, noting: 1. an inability to attribute symptoms like pain solely to RA, rather than also to other diagnoses or concurrent injury. 2. a desire to report only their more severe symptoms, rather than also monitoring minor health issues 3. a preference only to report data during periods of flare or instability, as reporting the absence of symptoms during periods of good health was felt to be unnecessary and a waste of time. “… we’ve had a few patients that have been saying, ‘I don’t know what to do about that bit of my pain because that pain I know is not related. So, do I include it or not include it’?” (HPI01) “… like sometimes my knee or my hip will be hurting, and I’ll think, well is it my rheumatoid, or is it because I’ve done a bit much, or is it a bit of osteo?” (PT05) “Pt reported questions were a bit monotonous. Pt has significant foot symptoms which weren't asked about.” (HPQ04) ““… daily feels slightly too much. I mean, I do appreciate with RA that it can literally change day-to-day. I do get that but from a user point of view, I think you could risk it being really genuine and reflective because it feels repetitive and it’s more like, ‘just get it done’, sometimes.” (PT15) “Well, I just put, ‘I’m fine’, and that’s why I’ve stopped doing it.” (PT01)			X
Symptom data collection	Engagement	Engagement was impacted both by intentional choice to report as much or as little as they wanted, and unintentionally forgetting to use the app, or otherwise missing the reminders. Others indicated that they were interested in the study, but had competing alternative health priorities (e.g. new diagnosis, health anxiety). “I just thought, this isn’t going to be a good idea for me, ‘cause I’m going to start to struggle with my health anxiety again and I didn’t want to go down that road.” (PT02) I tend to set a reminder on my phone… I think the reminder bit that’s built into the app doesn’t seem to work that well. (PT14) My problem now is I miss some days because I either miss the notification, if I don’t see it on my phone straight away… You don’t get more than one reminder. I do sometimes miss it, especially if I am working because I work until eight o’clock. It comes through quite early, the notification to do it. (PT18)			X
Symptom data use in clinic	Data availability	Clinicians reported that data were often absent or patchy. In one case it was noted that the participant was recording data in conflict with the experience they reported to their healthcare professional. “Everybody else has tended to, it’s just reinforced what they’re saying… the only one that’s changed is that one lady, because… I looked at her data before she arrived and I thought, ‘oh, she’s doing really well, this will be an easy consultation’… so she was probably the only one I’ve had where it’s not matched what she’s said.” (HPI01)			X	X
Symptom data use in clinic	Confidence and/or motivation to use the data	Interest in using the REMORA app data seemed variable. Several patients mentioned that they were interested in using the data in clinic, but they felt their clinicians were not. Conversely, some clinicians commented that their patients did not seem interested in the use of the data. “I had a rheumatology nurse appointment and I think she did look at it [REMORA dashboard], but after I said that I was on the trial. Whether she would have done or not, I honestly don’t know.“ (PT14) “I had one [appointment] a couple of weeks ago, but he didn’t really, he got all the data up on the screen, but he didn’t really sort of discuss it… I think it was helping him, ‘cause he said, oh brilliant, you know, I can see from this, you’ve filled it all in …but he didn’t sort of go into much more detail than that.” (PT19) “Patient did not seem very engaged in reviewing data but had been tracking” (HPQ06) “I’d turned the computer screen round and encouraged her to shuffle in to look at it. But she didn’t really, kind of, take the invitation. Whereas a previous patient had been quite excited and animated about the thought of looking at the data.” (HPI02) Clinicians highlighted that they would have appreciated more training in data interpretation and use. It was also noted that temporary technical issues prevented data being viewed in consultations in some instances. “I think that it would have been nice to have had some test data to have actually looked at in advance.” (HPI02) “…we’ve all struggled to understand what we’re looking at. We’ve had no formal, like, you know, even just an idiot’s guide… we had information about the study, and we had meetings about the study, all of us together, as a rheumatology team, but no information… about how to interpret it when we got it. We’ve just, sort of, figured it out.” (HIP01) “…the one thing I would’ve found beneficial is to be able to have a go at it myself.” (HPI07) “…so we know graphs, that’s fairly easy to understand but, obviously, you need to know whether going up’s good, or going down’s good…a couple of things that were on there weren’t things that I was au fait with, I don’t think.” (HPI05)			X	X
Infrastructure	-	The size of available participant pools were impacted by the geographical boundaries of sites and their case-mix (e.g. new diagnoses versus established and controlled diagnoses) of patients. It was felt that issues of staffing and resource availability at specific sites may have impacted on site interest, adherence with the app data, and collection of the primary outcome.	X		X	X
PDxx: Patient decliner interview, PNRxx: Patient not-registered (or on-boarded) interview, PTxx: Patient tracker interview, HPIxx: Healthcare professional interview, HPQxx: Healthcare professional free text note on symptom data use questionnaire

Table 4. Description of challenges and the feasibility domains they affected; (1) Recruitment and consent, (2) On-boarding, (3) Symptom tracking and review (4) Outcome measurement

[INSERT Table 4 HERE]

Clarity of communication and guidance

Site staff and patient participants brought up issues around the clarity of communication and guidance. For example, some patient participants reported problems with downloading or using the REMORA app due to a lack of communication from the study team or unclear/complex guidance. It was specifically highlighted that the duration of the active on-boarding window was unclear, leaving some participants unsure if they could still register after a delay.

The study materials did not include a comprehensive privacy statement, which may explain why some patients who declined participation raised concerns about privacy and data security (e.g. safety of personal data). Although some patients who consented but did not on-board shared these concerns, many stated they were not concerned about this. This suggests this primarily affected recruitment, and to a lesser extent on-boarding.

Accessibility

Issues of accessibility were defined as any issues impacting only specific groups of patients, such as those who were older, less technologically literate, or more unwell. Accessibility issues had an extensive impact on the on-boarding rates, with some more modifiable than others.

With regards to modifiable accessibility issues, difficulties were experienced among consented participants whose device did not meet the specific IT requirements (e.g., an older phone running an out-of-date operating system, lack of a PIN code on the device to prevent unauthorized individuals from accessing tracked symptom data). Not all participants were aware of these requirements at the time of recruitment, leading to some being consented but without being able to on-board to the study.

A second substantial challenge was that many participants indicated that they only checked emails irregularly or not at all, meaning that communications from the research team were often missed and therefore not actioned. For some participants, irregularly or not checking emails was normal, while others mentioned that this deviated from their normal email-checking behaviour because they had been busier than normal or had other competing priorities. Regardless, not reading the study instructions sent via email meant patient participants could not on-board.

Some participants also perceived issues surrounding the provision of on-boarding support: those who were more nervous about their ability to participate suggested they would have benefitted from greater support, while others had sought additional support from outside of the study team (e.g. from friends and family, or in clinic). Although participants could request support via email, some found it problematic that no telephone contact information was provided.

Patient participants felt that it could be difficult to integrate the study activities into their everyday life if they were busy, ill, or otherwise committed during the active on-boarding window. It was observed that the short active on-boarding window did not appropriately account for such events. Additional challenges included personal motivations and perceived benefits. Despite having consented to take part, several patients who did not on-board queried the specific benefits of the data collected within the app and its use in clinic, while others had not engaged with health apps in general or felt overwhelmed by how many were available.

Other, less modifiable personal barriers to participation included RA-related challenges (e.g., high levels of pain, dexterity, forgetfulness or brain fog) and non-RA related challenges (e.g., dyslexia, non-fluency in English, a lack of confidence with using technology).

Our mixed-methods feasibility trial demonstrated that our plans to evaluate REMORA within a multi-centre stepped wedge cluster randomised trial would be feasible with respect to recruitment, adherence, and outcome measurement. However, we also found that we require modifications to optimise intervention uptake, as just 62% of consented participants successfully completed on-boarding. The most likely threats to the success of the main trial included a lack of clarity of communication and guidance relating to downloading and using the app, as well as accessibility issues (e.g., technical and personal challenges).

Comparison to previous studies

In our study, the principal threat to the success of our proposed trial was intervention uptake. Previous studies have shown that the existence of digital divides are key barriers to uptake of smartphone apps among specific groups of people, affecting both access, use and benefits of mHealth solutions^[32]. For example, elderly individuals or those with lower socioeconomic backgrounds, have been shown to be more likely to lack the necessary equipment ^[33], than younger or more affluent counterparts. This may be a particular concern among those with RA, the prevalence and prognosis of which is associated with both age and deprivation^[34]. Here, we found that just over one-fifth of ineligible participants did not have access to a smartphone device at recruitment, and even among some who did and were willing to participate, their devices had operating systems that were too old to support the app. Furthermore, irregularity of checking emails, and a lack of familiarity with apps in general were reported to be barriers to uptake. While it was not possible in this study to link these issues directly to individuals’ demographics, it is likely that that some digital divides existed among our patient population.

A second key digital divide is that of eHealth literacy^[35], here used to refer to an individuals’ ability to find, create and/or use health related information on or from electronic platforms. Greater eHealth literacy is associated with a higher belief in the usefulness of solutions such as medical apps^[36], and it tends to be lower among older populations^{[37, 38]}. In this study, several interviewed patients identified themselves as feeling too nervous to use the app, or as otherwise lacking adequate skills to participate, without further support from the study team. Here, and elsewhere^{[8, 37, 39]}, it has been suggested that benefits may be obtained in providing more structured guidance and tailored education for patients and varied methods of contact with the study team including telephone and email. Evidence also indicates that physician recommendations influence the decision to adopt an apps^[39]. However, others have found that healthcare providers may be reluctant to use apps like REMORA due to rheumatologists' concerns that mHealth-based symptom monitoring may increase workloads^[40]. We note too that in this study both patients and clinicians expressed interest in using the app data during consultations but some perceived disinterest from the other party. This suggests that there may need to be further support to enable remote monitoring data to be used effectively as part of shared decision-making during consultations that could be included in training.

Study limitations

One limitation of our study was that, due to a lack of data on the date and approximate time of appointments, it was not possible to triangulate patients’ and professionals’ perceived use of symptom data with actual use of this data in clinic as recorded by the interactive REMORA dashboard. We have therefore modified our data collection tools to mitigate this in the proposed trial and allow us to record the date and (approximate) time of the consultation in which a patient is seen, as well as accessing the interactive REMORA dashboard data access logs, and both the patient and healthcare providers perception of data use.

A second limitation was that, because of time constraints and logistical challenges, we did not recruit patients to a standard-of-care group (as will be the case in the main trial to serve as the comparator group). This leaves it unknown if, and how, not receiving the intervention might have affected recruitment rates and outcome measurements, as well as whether there may be operational challenges in sites switching over from recruiting to standard-of-care to recruiting to integrated symptom tracking. Mitigations to try and alleviate concerns regarding this include blinding site staff involved in recruitment to the time of switch-over from standard of care to integrated symptom tracking, and extensive and continued engagement with all individuals involved in the implementation of the REMORA intervention.

We also note that a lack of translated versions of the app into different languages meant we could not recruit individuals who could not use, or be supported to use, an English version of REMORA. Acknowledging this limitation, we conducted a separate piece of work to understand barriers to participation among those who do not receive their healthcare in English. A manuscript for this work is currently in preparation. Nevertheless, in this study we made efforts to diversify the participants we interviewed including those who declined to participate in symptom tracking and we therefore believe our study can provide a representative overview of what all its intended users expect from an mHealth application.

Implications for trial design and conduct

This feasibility trial was designed to inform the delivery of a novel multi-centre stepped wedge cluster randomised trial, to evaluate the effectiveness of the REMORA intervention on key disease outcomes, including disease activity (i.e. DAS-28 or CDAI) and patient-reported outcomes, such as pain, fatigue and mood. This trial has been designed to overcome a number of key methodological challenges from available studies, such as small sample sizes, non-randomisation and use of low-tech interventions (e.g. web platforms, SMS services)^{[14–18, 41–44]}. Our findings show that it is feasible to overcome these limitations and contribute much needed evidence to determine the efficacy of remote symptom monitoring interventions.

The proposed REMORA trial will overcome many of these challenges via the recruitment of a large (~ 700) sample of patients from multiple outpatient rheumatology departments across two regions in the UK, randomised to either standard of care or integrated symptom tracking. Over 12 months, patients will complete daily, weekly, and monthly symptom reports via a bespoke co-designed app, which will be reviewed during routine visits requested by clinical care teams, rather than additional/dedicated research visits. Furthermore, following the delivery of this feasibility study, several key modifications have been made to our trial designwe have:

clarified our inclusion criteria, including the technical requirements of devices, to streamline recruitment of eligible participants;

taken steps to ensure potential participants are more fully informed about all processes required to initiate and maintain symptom tracking through co-produced patient information documentation; the development of video-based instructions, and provision of demonstration apps to recruiting teams at site;

elongated the “active on-boarding” window for people to join the study, to better reflect that individuals may experience delays in ability to on-board including competing interests or the experience of ill-health, and diversified the method of reminders provided to include telephone calls by the study team;

expanded the ways in which people can obtain support, including the use of a telephone, as well as email, helpdesk and peer support offered by our PPIE group. To further mitigate any issues with individuals infrequently accessing emails, we have also incorporated the use of text messages to alert participants to new email contacts, including welcome and reminder emails.

This study demonstrated that it would be feasible to conduct a stepped-wedge cluster-randomised trial to test the effectiveness of REMORA, a co-designed smartphone app with integration of tracked symptom data into medical records. We have shown that several challenges impacted on the availability and use of technologies for mobile health studies and intervention uptake. These findings ensured that we are equipped to provide optimised support to enhance the success of the trial and the implementation of the intervention being tested. We believe that the REMORA trial will contribute robust evidence to determine the impact of integrated symptom tracking on key care and disease outcomes among individuals with long-term conditions such as RA.

Ethics approval and consent to participate

The study received ethical approval from the UK Health Research Authority, South Central - Berkshire B Research Ethics Committee (ref: 22/SC/0103). All participants provided informed written or verbal consent prior to participation in the study.

Consent for publication

Not applicable

Competing Interests

WGD has received consultancy fees from Google, unrelated to this work. None of the other authors have any conflicts of interest to declare.

Funding

This work was funded by the National Institute for Health and Care Research (NIHR) under its Programme Grants for Applied Research (PGfAR) Programme (Grant Reference Number NIHR202030). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The work was also supported by infrastructure support from the Centre for Epidemiology Versus Arthritis (grant reference 20380), the NIHR Greater Manchester Patient Safety Research Collaboration (NIHR GM PSRC; NIHR204295) and the NIHR Manchester Biomedical Research Centre (NIHR BRC; NIHR203308).

Author Contribution

Conceptualization: KLD, YM, SS, DD, JM, CS, SvdV, WGD; Methodology: KLD, YM, SS, DD, JM, CS, SvdV, WGD; Formal analysis/Investigation: KLD, YM, SS, RDBS, DD, JM, CS, SvdV; Data curation: KLD, YM, SS, RDBS, DGJ, DD, JM, CS; Writing - Original Draft: KLD, YM, SvdV; Writing - Review & Editing: KLD, YM, SvdV, HC, SS, DGJ, RDBS, PB, JF, CAS, CJA, DD, JM, CS, WGD; Funding acquisition: SvdV, PB, JF, CAS, CJA, DD, JM, CS, WGD.

Acknowledgement

The authors would like to thank our Patient and Public Involvement and Engagement group and our collaborators involved in setting up and supporting the digital infrastructure. The REMORA team are: Sanaullah Alam, Mariam Al-Attar, Paul Amlani-Hatcher, Steven Antrobus, Michelo Banda, Nick Bansback, Benjamin Ellis, Sean Gavan, Felix Greaves, Rachel Heron, Elaine Mackey, Matthew Parkes, Alexandra Prodan, Karen Staniland, Charlotte Stockton-Powdrell, Yangzihan Wang, and Paul Wilson.

Data Availability

Requests for access to data and other study materials should be made in writing to the corresponding author.

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Competing interest reported. WGD has received consultancy fees from Google, unrelated to this work. None of the other authors have any conflicts of interest to declare.

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Preparing to deliver a stepped wedge cluster-randomised trial to test the effectiveness of daily symptom tracking integrated into electronic health records for managing rheumatoid arthritis: a mixed- methods feasibility trial

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BACKGROUND

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Participants

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Trial procedures under evaluation

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