Telehealth Follow-up in Patients With Rheumatoid Arthritis. A Noninferiority Randomized Control Trial
Phase: NA
Conditions: Rheumatoid Arthritis, Treatment Compliance
Sponsor: Vestre Viken Hospital Trust

 
 
 
 
 
 
 
 
 
 
 
Study title 
Telehealth Follow-up in Patients with Rheumatoid Arthritis. A 
Noninferiority Randomized Control Study (v2 26.03.2020) 
 
Short title 
Telehealth Follow-up in RA Patients 
REK Ref No 
33172 
Clinical.trials.gov  
Study Design 
Randomized Control Study 
Study 
Participants 
Patients with RA in remission or low disease activity 
Planned Sample 
Size 
195 
Planned Study 
Period 
One (1) year 
Main Objective 
The main objective of the present project is to test the effect of 
a 
customized 
Patient-Reported-Outcome 
(PRO)-based 
telehealth follow-up compared to a conventional pre-scheduled 
outpatient follow-up to monitor disease activity and expenses 
associated with the follow-up in patients with RA 
 

Telehealth Follow-up in Patients With Rheumatoid 
Arthritis. A noninferiority randomized control trial 
A collaborative project between Helse Sør-Øst, Martina Hansens 
Hospital and London School of Economics 
 
 
1. Project summary 
2. Introduction 
3. Objectives 
4. Research Methods 
5. Inclusion/exclusion criteria 
6. Ethical arrangements 
7. Risks, strengths and limitations 
8. Scientific competence for this project  
 
9. Publications and presentation of research data 
 
10. 
Budget 
 
11. 
Progress plan 
 
12. 
End of study 
 
13. 
References 
 
14. 
Appendix 
 
 
Synopsis 
 
1. Project summary: 

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease with a high burden both for 
the patient and society. Outcomes for the individuals living with RA have been improved and 
the majority of RA patients are now in remission or low disease activity status. RA patients 
continue to be followed up by pre-scheduled visits, which may compromise accessibility for 
the sickest and thus, quality of care. The introduction of patient-initiated follow-up leads to a 
reduction in the use of outpatient clinic services without compromising outcomes. Self-
monitoring and remote patient monitoring are facilitated by electronic innovative health tools. 
Among RA patients with low disease activity or remission, a Patient-Reported Outcome-
based telehealth follow-up for tight control of disease activity in RA can achieve similar 
disease control as conventional outpatient follow-up and is likely to reduce the costs. Thus, 
the main objective of the present project is to test the effect of a customized PRO-based 
telehealth follow-up compared to a conventional pre-scheduled outpatient follow-up to 
monitor disease activity and expenses associated with the follow-up in Norwegian RA 
patients.  
 
2. Introduction 
Rheumatoid arthritis (RA) is a common chronic inflammatory autoimmune disease with 
a significant burden for the patients. Impacting roughly 1% of the world’s population, RA is 
known for its polyarthritic involvement of the diarthrodial joints, affecting mainly the small 
joints of hands and feet [1]. The primary symptoms of synovitis (the inflammation of synovial 
membrane within joints) include pain, swelling, and stiffness of joints. Coupled with fatigue, 
another widely known symptom of RA, the patient’s quality of life (QoL) is tremendously 
diminished [2]. Indirect costs to patients including reduced work performance and hours, 
absenteeism, changing job roles or leaving employment is calculated to be 9,300 USD per 
patient [3]. Globally, the estimated total Years Lost to Disability (YLD) for RA increased from 

2,566,000 in 1990 to 3,776,000 in 2010, thus positioning RA as the 42nd highest disease 
contributor, and ranked between malaria and iodine deficiency [4].  
Recently, outcomes for the individuals living with RA have been improved, due to the 
discovery of novel treatments, such as biological agents, and treatment strategies, which enable 
early aggressive therapy through treat to target (T2T) strategies [5]. RA patients in remission 
achieve higher QoL  with improved physical functioning and higher work output compared 
with those who only reach low disease activity [6]. Thus, the indirect costs are lowest for RA 
patients attaining  remission  [6].  
However, the direct costs for a healthcare system to deliver optimal  treatments across 
their entire RA patient population has exponentially increased. In the United Kingdom, the 
estimated annual cost of treatment for the biological agents is approximately £ 9,500 
(approximately USD 13,900) per patient/year [7]. During the last 30 years, the number of RA 
patients on medication has increased because of  continuous development of innovative 
therapies. This presents a higher demand for closely monitoring of the patients in outpatients 
clinics. [8,9]. Hence, a conundrum is created: patients’ RA-related morbidity has decreased 
leading to reduced costs for inpatient care,  but with a consequently massive influx of patients 
into outpatient clinics [10].  
As a consequence of the rising health care direct costs, there is a need to optimizing and 
streamline the RA patients’ management. In Norway the majority of RA patients are diagnosed 
and initially treated in specialized hospital clinics based on the EULAR T2T strategy [11]. In 
one Norwegian outpatient clinic, in 2013, approximately 55% (DAS28; disease activity score) 
of the RA patients were reported to be in remission, and roughly 18% (DAS28) of the study 
group were in a low disease activity status [11].       
Given competing demands and scarce resources that are publicly funded in the Norwegian 
national health care system, it is of great importance to minimize unnecessary follow-up care 

which will be at  the expense of patients in need for outpatient clinic resources. This could be 
patients with new-onset disease or with arthritis flare [10]. The already implemented follow-up 
method based on pre scheduled visits in outpatient clinics, doubtfully corresponds with RAs 
unpredictable clinical course. In addition, pre scheduled visits are compromising accessibility 
for the sickest and thus, quality of care [12]. Monitoring patients with chronic conditions such 
as RA through telemedical instruments improve healthcare while reducing costs, adding value 
to the provided healthcare [12]. Furthermore, the reduced utilization of healthcare at the 
specialist level will release resources which could be allocated to non-responders and to rapid 
evaluation for the newly diagnosed patients or patients with a disease flare-up, a strategy that 
could result in even better outcomes [12].  
The introduction of patient-initiated follow-up leads to a reduction in the use of 
outpatient clinic services without compromising outcomes [12-14]. In other disease areas, such 
as diabetes, hypertension and congestive heart failure, patient self-monitoring is a well-accepted 
and common practice in supporting a tight control strategy and improves outcome [15]. Self-
monitoring and remote patient monitoring are facilitated by electronic innovative health tools. 
A recent double-blind multicenter randomized controlled study of 12 months follow-up showed 
that monitoring IBD patients via a telemedicine system was safe and reduced outpatient visits 
and hospital admissions compared with standard care [12].  
The use of innovative electronic tools has been recommended for close monitoring and 
follow up of patients with inflammatory arthritis [17]. For inflammatory arthritis, various 
remote monitoring tools are in use or being developed with the potential to help improve disease 
management [16,17]. Among RA patients with low disease activity or remission, a Patient 
Reported Outcome -based tele-health follow-up for tight control of disease activity in RA can 
achieve similar disease control as conventional outpatient follow-up and is likely to reduce the 
costs [16]. In patients with RA, as high as 70% were reported not to adhere to their prescribed 

treatment [13]. Modern electronic tools may also improve medication compliance by reminding 
the patient to take their prescribed drugs. However, the beneficial effects of the telehealth 
follow-up are not limited to health care improvement at the patient level. It is believed that the 
introduction of novel follow-up strategies in chronic diseases will result in a reduction of the 
costs related to health care till the end of 2025 for the Norwegian Healthcare system up to 23 
billion Norwegian Crowns  [18].  
3. Study Objectives 
Study hypothesis 
A customized Patient-Reported Outcome (PRO)-based telehealth follow-up to monitor 
disease activity in Norwegian patients with RA is noninferior to standard care (prescheduled 
outpatient follow-up) and reduces the use of health care resources. 
Main Objective 
The main objective of the present project is to compare the effect of a customized PRO-based 
telehealth follow-up compared to a conventional pre-scheduled outpatient follow-up to 
monitor disease activity and expenses associated with the follow-up in each group, in 
Norwegian patients with RA. 
- 
Primary outcome: Occurrence of flare within 52 weeks, defined as a RAPID3 score of 3 
or more at any measurement between randomization and 52 weeks. 
Secondary objectives 
- 
Potentially identify patients with a higher risk of flare by examining a core set of PROs  
(MHAQ, RAPID3, EQ-5D, Visual analog Scale (VAS) Total pain, VAS satisfaction with 
the follow-up) in both groups. 
- 
Compare satisfaction rates (as measured by VAS satisfaction with the follow-up 
procedures) among the 2 patient groups 
- 
CDAI for both groups at baseline and at 52 weeks. 

- 
Correlation between RAPID 3 and CDAI at 52 weeks 
4. Study design, data collection 
This is a pragmatic noninferiority randomized controlled trial (RCT) in Norwegian RA 
patients in clinical remission (based on RAPID3 score). The RA patients will be recruited at 
the department of Rheumatology MHH in Bærum, Norway.  Only ACPA and/or RF positive 
patients will be included. The patients will be allocated to 2 groups. One group will be 
followed up by an electronic app, while the second group will attend conventional pre-
scheduled visits in the outpatient clinic of MHH. A computer-generated random number 
sequence will be used for randomization of the RA patients. 
 
The primary outcome is occurrence of flare within 52 weeks, defined as a RAPID3 score of 3 
or more at any measurement between randomization and 52 weeks.   
 
RAPID3 (routine assessment of patient index data 3) is a pooled index of the 3 patient-
reported American College of Rheumatology rheumatoid arthritis (RA) Core Data Set 
measures: function (MHAQ), pain, and patient global estimate of status. Each of the 3 
individual measures is scored 0 to 10, for a total of 30 (appendix). 
- 
At the baseline, the following data will be collected: RAPID3, MDHAQ, age, gender, 
disease duration, ACPA, RF, EQ-5D, Visual analog Scale Total pain, VAS satisfaction, 
CDAI and medications. Data will be collected every 6th  month for a period of 1 year.  
- 
CDAI for both groups will be registered at 52 weeks 
 
An electronic app (MyDigno) available both on Apple store and Android play which the 
patients can download for free will be used to collect data from the Telehealth follow up 
group. After the patient signs the informed consent, she/he will be invited to download the 

app. The data which will be collected via the app will be encrypted and stored in Dignio’s 
server. The Go Treat It program will be used to collect data of the conventional follow-up 
outpatient clinic group (Appendix 3). The data collected by the GTI are stored in MHH 
servers inside the firewall of the HSØ. 
According to the treat-to-target strategy, all included patients, irrespective of group allocation, 
will be allowed access to extraordinary outpatient visits if needed.  The patients can contact 
the study nurse, via the app or by phone. Extra visits will be registered, and RAPID3 score 
will be registered. All the patients suffering a flare during the study period will be registered 
in both groups.   We expect that approximately 20% of the included patients will experience a 
flare at least once during follow up. 
A cost-effectiveness analysis will also be performed.  The economic evaluation will be 
conducted alongside the randomised controlled trial on telehealth follow-up in patients with 
rheumatoid arthritis (RA) to examine the difference in costs and outcomes between a 
telehealth patient follow-up strategy and a standard pre-scheduled follow-up strategy. The 
objective is to estimate the incremental cost-effectiveness ratio (ICER) of the more effective 
follow-up strategy (if it is associated with higher costs) or the total cost savings associated 
with the less expensive follow-up strategy (if it is at least as effective as the more expensive 
follow-up strategy). The study hypothesis is that a customised patient-reported outcome 
(PRO)-based telehealth follow-up strategy to monitor disease activity in patients with RA is 
non-inferior to standard care (Appendix 2).  
5. Inclusion/exclusion criteria 
Inclusion criteria 
1. >18 years 
2. Fulfilling EULAR/ACR 2010 classification criteria for RA  
3. Being in remission (RAPID 3 score <=3) 
4. Able to use the electronic app 

5. Able to give informed consent 
Exclusion criteria 
1. <18 years 
2. Severe cognitive failure 
 
6. Statistical considerations 
All statistical analyses will be detailed in a separate Statistical Analysis Plan (SAP). 
 
6.1 Statistical Hypotheses 
This study is designed to test the null hypothesis that the standard pre-scheduled outpatient 
follow-up scheme (RRO-OC, Rheumatologist Reported Outcome – Outpatient Clinic) is 
inferior to the customized PRO-based telehealth follow-up (PRO-TH, Patient Reported 
Outcome - TeleHealth) in monitoring disease activity in Norwegian RA patients. The 
alternative hypothesis is that PRO-TH is non-inferior to the RRO-OC by a pre-specified margin, 
on the expectation that the non-inferiority of PRO-TH is sufficient to fulfil the demands for 
sustained control of the disease activity in RA. Non-inferiority is established for the RAPID 3 
score. In statistical terms: 
 
where RRO-OC is the success proportion of the standard follow-up (patients in sustained 
remission at week 52),  PRO-TH is the success proportion of the telehealth follow-up, and  is 
the non-inferiority margin. 
6.2 Sample Size Determination 
The sample size determination is based on comparing proportions for the binary outcome 
variable describing patients remission status at week 52 of the trial (remission: RAPID 3 < 3, 
increased disease activity: RAPID 3  3) in a non-inferiority setting. Based on the literature 
available at the study’s initiation, the proportion of the RA patients in sustained remission at 
week 52 after initial is assumed to be approximately 80% (equivalent to 20% increase in disease 
activity, ie. flare). The success proportion is assumed to be equal for both arms of the trial. A 
difference of 15% in the proportion of patients suffering a flare between the two follow-up 
groups is considered acceptable for the non-inferiority. Accordingly, a sample size of N=176 

patients (n=88 per RRO-OC/PRO-TH arm) is expected to achieve a power of 80% (p<0.05, one 
sided) if the true remission rate difference at week 52 between the RRO-OC and PRO-TH 
groups is 15% (Table 6.1). Table 6.1 shows how deviations from these assumptions affect the 
sample sizes required to achieve a power of 80%. Considering a dropout rate of 10%, a total of 
approximately195 patients will be enrolled.  
Table 6.1: Sample size required for a non-inferiority trial, with different assumptions about the non-inferiority 
margin and success proportion, assuming a significance level of 5% and a power of 80%. An equal number of 
patients in each trial arm is assumed. 
 
Non-
inferiority 
margin (%) 
Success proportion (patients in remission at week 52) (%) 
70 
75 
80 
85 
10 
520 
464 
396 
316 
15 
232 
208 
176 
142 
20 
130 
116 
100 
80 
 
 
6.3 Randomization 
After all applicable screening assessments have been performed, patients who have met all 
inclusion criteria and none of the exclusion criteria will be randomly allocated to one of the 
follow-up groups (PRO-TH or RRO-OC).  
6.4 Populations for Analyses 
 
For purposes of analysis, the following populations are defined: 
 
Population 
Description 
Enrolled  
All participants who fulfil the study inclusion criteria and sign 
the informed consent form. 
Randomly Assigned to 
Study Intervention 
All enrolled patients who are randomized according to 
randomized intervention assignment (intervention = PRO-
TH/RRO-OC). This set will be analyzed ignoring 
noncompliance, protocol deviations, withdrawal, and anything 
that happens after randomization. 
Evaluable 
All enrolled patients who are randomized according to the 
randomized intervention assignment and followed-up 
according to the planned randomization. 
Safety 
All enrolled patients who are randomly assigned to 
intervention according to the planned randomization. 
Participants will be analysed according to the intervention they 
actually received. 
 

6.5 Statistical Analyses 
 
The statistical analysis plan will be developed and finalized before database lock and will 
describe the participant populations to be included in the analyses, and procedures for 
accounting for missing, unused, and spurious data. This section is a summary of the planned 
statistical analyses of the primary and secondary endpoints. 
6.5.1 Efficacy Analyses 
 
Endpoint 
Statistical Analysis Methods 
Primary 
Disease activity status of the patient “RAPID 3 < 3/ RAPID 3  3”, is 
assessed by default three times during the trial period at baseline, 6 months, 
and 12 months, and additionally upon patient request. The binary primary 
endpoint variable gets then value 1 if the RAPID 3 score has been  3 once 
or more at any of the time points, and 0 if  RAPID 3 < 3 at all time points. 
To demonstrate the non-inferiority of the PRO-TH follow-up, this binary 
primary endpoint variable is modelled using a logistic regression model 
with randomized treatment group as factor, adjusted for baseline RAPID3 
measurement. Patients with no (missing) RAPID3 measurements after 
baseline will be imputed with worst outcome (not in remission).  
Secondary 
Continuous secondary endpoints MHAQ, RAPID 3, EQ-5D, and Visual 
analog scale (VAS) total pain will be analysed using linear mixed models 
accounting for the correlations between repeated measurements (baseline, 6 
months, 12 months) for each patient with intervention and visit as fixed 
factors, the respective baseline score as covariate, and allowing for an 
intervention by visit interaction. A patient with missing data on the 
remission status will be assumed to be not in remission. 
Exploratory 
Will be described in the statistical analysis plan finalized before database 
lock. 
  
6.5.2 Safety Analyses 
 
All safety analyses will be performed on the Safety Population. 
 
Endpoint 
Statistical Analysis Methods 
Primary 
Summary tables will be created for all safety information. All site-reported 
AEs will also be grouped by seriousness (SAE vs. nonserious AE), primary 
relationship (intervention, unrelated, and unknown) and timing of onset 
(pre- and post intervention).  
Secondary 
 
Exploratory 
Will be described in the statistical analysis plan finalized before database 
lock. 

 
6.5.3 Other Analyses  
 
Secondary objective is to identify patients with higher risk of flare by examining a core set of 
PROs including MHAQ, RAPID3, EQ-5D, and Visual analog Scale (VAS) Total pain. Each 
PRO is measured repeatedly at baseline, 6 months and 12 months. The risk factors are identified 
by fitting a logistic regression using GEE accounting for the correlations between repeated 
measurements for each patient, with a dichotomous endpoint “RAPID 3 < 3/ RAPID 3  3”, all 
continuous PROs as predictors, and the respective continuous baseline disease activity score 
(RAPID 3) as covariate. The parameter space will be explored to find a combination parameter 
values predicting a risk of increased disease activity. A patient with missing data on the 
remission status will be assumed to be not in remission.  
It is also of interest to compare satisfaction rates (as measured by VAS satisfaction with the 
follow-up procedures) among the two intervention groups. This will be assessed using 
ANCOVA model adjusted for baseline disease activity score (RAPID 3). 
Third, the mean change in continuous CDAI disease activity index between baseline and 52 
weeks is compared between the two intervention groups using an ANCOVA model adjusted 
for baseline CDAI. In addition, the correlation between RAPID 3 and DAS 28 scores at 52 
weeks will be computed. 
 
Finally, an economic evaluation will be performed alongside the randomised controlled trial to 
examine the difference in costs and outcomes between PRO-TH and RRO-OC groups. 
(Appendix 2) 
6.6 Interim Analyses  
No interim analyses involving formal statistical testing of the study hypotheses are planned.  
 
7. Ethical considerations 
Potential participants will be approached by the study nurse and provided with a 
participant/patient information sheet. All participants will be given the opportunity to discuss 
the research project before obtaining informed consent or assent.  

The confidentiality will be maintained by using a study patient number for each participant. 
Only the project leader groupwill know the identity of the patient. Electronic information will 
be identified only through this non-identifiable study code.  Information on study subjects will 
be stored in a locked electronic file in Martina Hansens Hospital server for research.  All data 
will be managed and analyzed anonymously. All reports will be of a summary nature and no 
individuals could be identified.  
All recruited participants will be older than 18 years. We will include every person who 
qualifies for inclusion regardless of race, ethnicity or socioeconomic status. 
This project does not involve any experimentation on human subjects.  All subjects will 
undergo a conversation with the study nurse before inclusion, where they will be given 
information about the study and that they at all time can quit the study.   
8. Risks  
We expect that  approximately 20% of the included patients will experience  a flare at least 
once during follow-up . According to the treat-to-target strategy, all included patients, 
irrespective of group allocation, will be allowed access to extraordinary outpatient visits if 
needed to evaluate disease activity and modify treatment. 
In the very unusual situation in which the electronic app is not available for the patients due to 
technical challenges, the patients can contact the study nurse directly by telephone. 
 
9. Scientific competence for this project  
Andreas P Diamantopoulos: Rheumatologist with 10 years experience in treatment and 
follow-up of RA patients. Master in Public health from UC Berkeley and  Currently studying 
at LSE health economics. 
Anne Bull Haaversen: Rheumatologist with 8 years experience in the treatment and follow-
up of RA patients.  
Jannicke Karlengen: Nurse with broad experience in the follow up of patients with RA, 
experience with the EXODUS app. 
Oslo University Unit for clinical Trials provides statistical support for the study 

10. Publications and presentation of research data 
The research data will be presented to the Health Trust of South-Eastern Norway and 
published in peer-review journals. 
 
 
 
 
 
 
 
11. Budget 
 
Budget Telehealth follow-up study 
 
 
 
 
Budsjett 
Salary (including social costs) 
->Physician 50%  
1 000 
000 
-> Nurse 100% 
900 000 
-> Clerk 20% of nurse salary 
140 000 
Materials 
300 000 
Health-economic analysis (LSE) 
100 000 
Visits 
-> London School of Economics (LSE) 
101 000 
-> Kaiser Permanente (California) 
129 000 
Conference HSØ 
150 000 
Diverse costs (15%) 
300 000 
Total 
3 120 
000 
 
 
12. Progress plan 
 
 
* 
Year 1 
Year 2 
 
 month 
month 
month 
month 
month 
month 
 
month 
  
     
month 
 
month 
 
month 
 
month 
 
month 

Get REC 
approval 
 
 
 
 
 
 
      
 
 
 
 
 
 
Develop 
cohort study 
documentatio
n 
/SOPs 
 
 
 
 
 
 
 
 
 
 
 
 
 
Cohort study 
recruitment 
 
 
 
 
 
 
 
 
 
 
 
 
 
Patients’ 
monitoring 
 
 
 
 
 
 
 
 
 
 
 
 
 
Database 
final cleaning 
& locking 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data analysis 
 
 
 
 
 
 
 
 
 
 
 
 
 
Preparation 
of final report 
& 
manuscripts 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
      
13. End of the study 
The period of recruitment and inclusion of patients is expected to last for 3 months. Written 
informed consent will be obtained by the project nurse and kept in a password locked 
 
 
14. References 
 
1. Put S, Westhovens R, Lahoutte T, Matthys P (2014) Molecular imaging of rheumatoid 
arthritis: emerging markers, tools, and techniques. Arthritis Res Ther 16: 208. 
2. Whalley D, McKenna SP, de Jong Z, van der Heijde D (1997) Quality of life in rheumatoid 
arthritis. Br J Rheumatol 36: 884-888. 
3. Li X, Gignac MA, Anis AH (2006) The indirect costs of arthritis resulting from 
unemployment, reduced performance, and occupational changes while at work. Med 
Care 44: 304-310. 
4. Cross M, Smith E, Hoy D, Carmona L, Wolfe F, Vos T, et al. (2014) The global burden of 
rheumatoid arthritis: estimates from the global burden of disease 2010 study. Ann 
Rheum Dis 73: 1316-1322. 

5. Markusse IM, Akdemir G, Dirven L, Goekoop-Ruiterman YP, van Groenendael JH, Han 
KH, et al. (2016) Long-Term Outcomes of Patients With Recent-Onset Rheumatoid 
Arthritis After 10 Years of Tight Controlled Treatment: A Randomized Trial. Ann 
Intern Med 164: 523-531. 
6. Radner H, Smolen JS, Aletaha D (2014) Remission in rheumatoid arthritis: benefit over 
low disease activity in patient-reported outcomes and costs. Arthritis Res Ther 16: 
R56. 
7. NICE Commissioning biologic drugs for the treatment of inflammatory disease in 
rheumatology, dermatology and gastroenterology. 
8. McBain H, Shipley M, Olaleye A, Moore S, Newman S (2015) A patient-initiated 
DMARD self-monitoring service for people with rheumatoid or psoriatic arthritis on 
methotrexate: a randomised controlled trial. Ann Rheum Dis. 
9. Kvalvik AG, Larsen S, Aadland HA, Hoyeraal HM (2007) Changing structure and 
resources in a rheumatology combined unit during 1977-1999. Scand J Rheumatol 36: 
125-135. 
10. Diamantopoulos AP, Brown TT. Transforming Healthcare in Rheumatology: From 
Inpatient to Outpatient Care without Reductions in Healthcare Quality; 2016. WILEY 
111 RIVER ST, HOBOKEN 07030-5774, NJ USA. 
11. Haugeberg G, Hansen IJ, Soldal DM, Sokka T (2015) Ten years of change in clinical 
disease status and treatment in rheumatoid arthritis: results based on standardized 
monitoring of patients in an ordinary outpatient clinic in southern Norway. Arthritis 
Res Ther 17: 219. 
12. de Jong MJ, van der Meulen-de Jong AE, Romberg-Camps MJ, Becx MC, Maljaars JP, 
Cilissen M, et al. (2017) Telemedicine for management of inflammatory bowel disease 

(myIBDcoach): a pragmatic, multicentre, randomised controlled trial. Lancet 390: 
959-968. 
13. McBain H, Shipley M, Olaleye A, Moore S, Newman S (2016) A patient-initiated 
DMARD self-monitoring service for people with rheumatoid or psoriatic arthritis on 
methotrexate: a randomised controlled trial. Ann Rheum Dis 75: 1343-1349. 
14. Hewlett S, Kirwan J, Pollock J, Mitchell K, Hehir M, Blair PS, et al. (2005) Patient 
initiated outpatient follow up in rheumatoid arthritis: six year randomised controlled 
trial. BMJ 330: 171. 
15. Smolen JS, Breedveld FC, Burmester GR, Bykerk V, Dougados M, Emery P, et al. (2016) 
Treating rheumatoid arthritis to target: 2014 update of the recommendations of an 
international task force. Ann Rheum Dis 75: 3-15. 
16. de Thurah A, Stengaard-Pedersen K, Axelsen M, Fredberg U, Schougaard LMV, Hjollund 
NHI, et al. (2018) Tele-Health Followup Strategy for Tight Control of Disease 
Activity in Rheumatoid Arthritis: Results of a Randomized Controlled Trial. Arthritis 
Care Res (Hoboken) 70: 353-360. 
17. van Riel P, Alten R, Combe B, Abdulganieva D, Bousquet P, Courtenay M, et al. (2016) 
Improving inflammatory arthritis management through tighter monitoring of patients 
and the use of innovative electronic tools. RMD Open 2: e000302. 
18. Accenture (2019) DIGITALE LEGETIMER SKYTER FART – ER DET RIKTIG 
BEHANDLING? 
19. Pincus T, Yazici Y, Bergman M, Swearingen C, Harrington T (2006) A proposed 
approach to recognise "near-remission" quantitatively without formal joint counts or 
laboratory tests: a patient self-report questionnaire routine assessment of patient index 
data (RAPID) score as a guide to a "continuous quality improvement" s. Clin Exp 
Rheumatol 24: S-60-65; quiz S-66-73. 

14. Appendices 
 
1. RAPID 3 score (0-30) [19] 
 
- 
0 to 3: near remission, 
- 
3.1 to 6: low severity 
- 
6.1 to 12 : moderate severity 
- 
>12.1 : high severity 
 
2. Telehealth follow-up in patients with rheumatoid arthritis: 
protocol for economic evaluation 
 
1. Background  
 
The economic evaluation will be conducted alongside the randomised controlled trial on 
telehealth follow-up in patients with rheumatoid arthritis (RA) to examine the difference in 
costs and outcomes between a telehealth patient follow-up strategy and a standard pre 
scheduled follow-up strategy. The objective is to estimate the incremental cost-effectiveness 
ratio (ICER) of the more effective follow-up strategy (if it is associated with higher costs) or 
the total cost savings associated with the cheaper follow-up strategy (if it is at least as 
effective as the more expensive follow-up strategy). The study hypothesis is that a customised 
patient reported outcome (PRO)-based telehealth follow-up strategy to monitor disease 
activity in patients with RA is non-inferior to standard care. 
 
2. Methods 
 
2.1 Population, setting and location, and comparator 
 
The study population will comprise of RA patients in clinical remission (based on RAPID3 
score). The study setting will be the Department of Rheumatology MHH in Bærum, Norway. 
One group of patients will be followed-up using a telehealth strategy and a second group will 
attend conventionally scheduled follow-up outpatient appointments at MHH. 
 

2.2 Type of economic evaluation  
 
Cost-utility analyses (CUA) will be used for the economic evaluation with quality-adjusted 
life years (QALYs) as the outcome measure. 
 
2.3 Time horizon  
 
Two CUAs will be considered: a within-trial economic evaluation using information collected 
during a 1-year follow-up period and an economic evaluation based on a long-term model. 
 
2.4 Study perspective 
 
This economic evaluation will adopt the Norwegian healthcare system and societal 
perspectives. 
 
2.5 Discount rate 
 
The base case analysis rate that will be used for discounting future costs and effects will be 
4%. 
 
2.6 Identification, measurements and valuation of outcomes 
 
The EuroQol five-dimensions five level (EQ-5D-5L) will be used to assess the quality of life 
(QoL) for each patient. EQ-5D-5L data will be collected at baseline and followed up every 6 
months for a period of 1 year.  The utility weights for each health state at different periods 
will be obtained by using an EQ-5D index tariff appropriate for the Norwegian population. 
Mean differences in EQ-5D between the groups will be estimated and will be presented with 
statistical tests of significance for the different follow-up periods. The potential imbalances in 
baseline utility will be adjusted to estimate differential mean QALYs. 
 
2.7 Identification, measurement and valuation of resource use 
 

The primary objective of the cost analysis will be identifying, quantifying and valuing 
resource use accompanying the RA patient follow-up strategies. Data on the resources 
required to set up and run the PRO-based telehealth monitoring system will be recorded as 
part of the trial process. Data will be collected for the duration of the trial on participant use of 
healthcare services including hospital services, primary care, medicine and community care 
services to estimate the effect of the two follow-up strategies on resource use from the 
healthcare system perspective. Costs for each type of resource use unit will be calculated from 
sources appropriate to Norway to produce estimates of the cost of each type of resource use at 
the start and end of the trial for each trial arm. To estimate costs from a societal perspective, 
data on loss of days of work (number of working hours foregone) will be collected using 
questionnaires. The cost of productivity loss for the trial patients will be calculated using a 
human capital approach.  
 
2.8 Modelling 
 
A pre-trial decision model will be used for the long-term analysis. The modelling exercise 
will be from the Norwegian perspective and the population that will be considered in the 
model will be similar to the patients included in the trial. A 2-step modelling strategy will be 
adopted, where  the model will be based on a decision tree for the first year after 
randomisation and then a Markov model will be used to assess the health states of the patients 
for their lifetime period.  
 
2.8.1 Model parameters 
 
The Markov model will be populated with transition probabilities and with cost and utility 
data conditional on health status. Values of the effectiveness parameters, transition 
probabilities of moving from health states and utilities will be based on the review of selected 
studies on clinical effectiveness and economic evaluation studies, previous reviews and expert 
advice from clinicians. Statistical analysis of the trial data will provide more information to 
generate robust values for the pre-trial model parameters. The validation of the model will be 
conducted by looking at the internal and external validity. 
 
 

3. Analysis  
 
3.1 Within-trial analysis 
 
Statistical analyses and CUAs will be carried out using trial data where information on a 
range of resource use and outcome measures are collected at the patient level. The basis of the 
analysis will be intention to treat. 
 
3.1.1 Handling missing data 
 
Appropriate methods, such as multiple imputations, will be used to treat missing information. 
A descriptive analysis will be undertaken to check the nature of missing data before such a 
method is used. In addition, a sensitivity analysis will be conducted to examine the impact of 
alternative assumptions about the mechanism of missing data.  
 
3.1.2 Base case analysis and regression  
 
Baseline characteristics of the patients in the two different follow-up groups will be 
summarised. Differences in resource use and costs between the two groups will be tested 
using two-sample t-test (or non-parametric equivalents) and X2 test for continuous and 
categorical variables, respectively. The mean costs of resource use in each trial arm and the 
differences in costs between the two arms will be calculated with 95% CIs. Similarly, the 
mean QALY score for each group will be estimated.  
Regression analysis will be conducted to examine how the total cost and health outcomes may 
be explained by the patient characteristics and follow-up strategy.  Different types of models 
will be explored within a generalized estimating equation framework.  
 
3.1.3 CUA with trial data 
 
CUA will be presented as incremental cost per QALY gained. The ICER will be calculated 
as: 
 
𝐼𝐶𝐸𝑅= 𝐶𝑜𝑠𝑡𝑡𝑒𝑙𝑒− 𝐶𝑜𝑠𝑡𝑠𝑐
𝑄𝐴𝐿𝑌𝑡𝑒𝑙𝑒− 𝑄𝐴𝐿𝑌𝑠𝑐
 

 
where  𝐶𝑜𝑠𝑡𝑡𝑒𝑙𝑒 is the total cost of the telehealth patient follow-up group and 𝐶𝑜𝑠𝑡𝑠𝑐 is the 
total cost of the standard care group. Similarly, 𝑄𝐴𝐿𝑌𝑡𝑒𝑙𝑒 is total QALYs for the telehealth 
patient follow-up group and 𝑄𝐴𝐿𝑌𝑠𝑐 is total QALYs for the standard care patients.  
Subgroup analyses (e.g., gender, age) will be considered to address potential issues of 
underlying heterogeneity.  The results of the cost-effectiveness including the subgroup and 
sensitivity analysis will be presented both in terms of point estimates and cost-effectiveness 
planes, and cost-effectiveness acceptability curves (CEACs). 
The two follow-up strategies will also be compared on the basis of incremental net monetary 
benefits. The net monetary benefit of the telehealth patient follow-up strategy or standard care 
strategy will be calculated as the mean QALYs multiplied by the acceptability threshold 
values for a QALY, minus the mean cost of implementing the randomized. An appropriate 
QALY threshold value will be chosen for Norway.  
 
3.2 The effectiveness measure and CEA in the model-based analysis 
 
The effectiveness measure for economic outcomes in our model-based analysis will be 
QALYs. Model results will provide estimates for the ICER considering long-term health 
outcomes and costs. The results of the economic evaluation including the subgroup and 
sensitivity analyses will be presented in terms of point estimates, cost-effectiveness planes 
and CEACs.  
 
3.3 Handling uncertainty 
 
To present the robustness of the estimation of cost-effectiveness, non-parametric 
bootstrapping techniques will be used. CEACs will illustrate the uncertainty surrounding the 
estimate of cost-effectiveness. Both probabilistic and deterministic sensitivity analyses will be 
used to explore statistical and other forms of uncertainty arising from the imprecision with 
which model parameters are estimated.  
 
3. EXODUS app safety presentation 

Pasientapp 
EXODUS_SIkkerhet.pd