scholarly journals A Mobile App for Longterm Monitoring of Narcolepsy Symptoms: Design, Development, and Evaluation

10.2196/14939 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e14939
Author(s):  
Laury Quaedackers ◽  
Jan De Wit ◽  
Sigrid Pillen ◽  
Merel Van Gilst ◽  
Nikolaos Batalas ◽  
...  
Keyword(s):  
Interaction Design ◽  
Daytime Sleepiness ◽  
Patient Reported Outcome ◽  
Mobile App ◽  
Design Development ◽  
Subjective Burden ◽  
Design Data ◽  
Patient Reported ◽  
Depth Interviews ◽  
Over Time

Background Narcolepsy is a chronic sleep disorder with a broad variety of symptoms. Although narcolepsy is primarily characterized by excessive daytime sleepiness and cataplexy (loss of muscle control triggered by emotions), patients may suffer from hypnagogic hallucinations, sleep paralysis, and fragmented night sleep. However, the spectrum of narcolepsy also includes symptoms not related to sleep, such as cognitive or psychiatric problems. Symptoms vary greatly among patients and day-to-day variance can be considerable. Available narcolepsy questionnaires do not cover the whole symptom spectrum and may not capture symptom variability. Therefore, there is a clinical need for tools to monitor narcolepsy symptoms over time to evaluate their burden and the effect of treatment. Objective This study aimed to describe the design, development, implementation, and evaluation of the Narcolepsy Monitor, a companion app for long-term symptom monitoring in narcolepsy patients. Methods After several iterations during which content, interaction design, data management, and security were critically evaluated, a complete version of the app was built. The Narcolepsy Monitor allows patients to report a broad spectrum of experienced symptoms and rate their severity based on the level of burden that each symptom imposes. The app emphasizes the reporting of changes in relative severity of the symptoms. A total of 7 patients with narcolepsy were recruited and asked to use the app for 30 days. Evaluation was done by using in-depth interviews and user experience questionnaire. Results We designed and developed a final version of the Narcolepsy Monitor after which user evaluation took place. Patients used the app on an average of 45.3 (SD 19.2) days. The app was opened on 35% of those days. Daytime sleepiness was the most dynamic symptom, with a mean number of changes of 5.5 (SD 3.7) per month, in contrast to feelings of anxiety or panic, which was only moved 0.3 (SD 0.7) times per month. Mean symptom scores were highest for daytime sleepiness (1.8 [SD 1.0]), followed by lack of energy (1.6 [SD 1.4]) and often awake at night (1.5 [SD 1.0]). The personal in-depth interviews revealed 3 major themes: (1) reasons to use, (2) usability, and (3) features. Overall, patients appreciated the concept of ranking symptoms on subjective burden and found the app easy to use. Conclusions The Narcolepsy Monitor appears to be a helpful tool to gain more insight into the individual burden of narcolepsy symptoms over time and may serve as a patient-reported outcome measure for this debilitating disorder.

scholarly journals A Mobile App for Longterm Monitoring of Narcolepsy Symptoms: Design, Development, and Evaluation (Preprint)

2019 ◽  
Author(s):  
Laury Quaedackers ◽  
Jan De Wit ◽  
Sigrid Pillen ◽  
Merel Van Gilst ◽  
Nikolaos Batalas ◽  
...  
Keyword(s):  
Interaction Design ◽  
Daytime Sleepiness ◽  
Patient Reported Outcome ◽  
Mobile App ◽  
Design Development ◽  
Subjective Burden ◽  
Design Data ◽  
Patient Reported ◽  
Depth Interviews ◽  
Over Time

BACKGROUND Narcolepsy is a chronic sleep disorder with a broad variety of symptoms. Although narcolepsy is primarily characterized by excessive daytime sleepiness and cataplexy (loss of muscle control triggered by emotions), patients may suffer from hypnagogic hallucinations, sleep paralysis, and fragmented night sleep. However, the spectrum of narcolepsy also includes symptoms not related to sleep, such as cognitive or psychiatric problems. Symptoms vary greatly among patients and day-to-day variance can be considerable. Available narcolepsy questionnaires do not cover the whole symptom spectrum and may not capture symptom variability. Therefore, there is a clinical need for tools to monitor narcolepsy symptoms over time to evaluate their burden and the effect of treatment. OBJECTIVE This study aimed to describe the design, development, implementation, and evaluation of the Narcolepsy Monitor, a companion app for long-term symptom monitoring in narcolepsy patients. METHODS After several iterations during which content, interaction design, data management, and security were critically evaluated, a complete version of the app was built. The Narcolepsy Monitor allows patients to report a broad spectrum of experienced symptoms and rate their severity based on the level of burden that each symptom imposes. The app emphasizes the reporting of changes in relative severity of the symptoms. A total of 7 patients with narcolepsy were recruited and asked to use the app for 30 days. Evaluation was done by using in-depth interviews and user experience questionnaire. RESULTS We designed and developed a final version of the Narcolepsy Monitor after which user evaluation took place. Patients used the app on an average of 45.3 (SD 19.2) days. The app was opened on 35% of those days. Daytime sleepiness was the most dynamic symptom, with a mean number of changes of 5.5 (SD 3.7) per month, in contrast to feelings of anxiety or panic, which was only moved 0.3 (SD 0.7) times per month. Mean symptom scores were highest for daytime sleepiness (1.8 [SD 1.0]), followed by lack of energy (1.6 [SD 1.4]) and often awake at night (1.5 [SD 1.0]). The personal in-depth interviews revealed 3 major themes: (1) reasons to use, (2) usability, and (3) features. Overall, patients appreciated the concept of ranking symptoms on subjective burden and found the app easy to use. CONCLUSIONS The Narcolepsy Monitor appears to be a helpful tool to gain more insight into the individual burden of narcolepsy symptoms over time and may serve as a patient-reported outcome measure for this debilitating disorder.

scholarly journals AB0051 THE EFFECTS OF NON-INVASIVE VAGUS NERVE STIMULATION ON IMMUNOLOGICAL RESPONSES AND PATIENT REPORTED OUTCOME MEASURES OF FATIGUE IN PATIENTS WITH CHRONIC FATIGUE SYNDROME, FIBROMYALGIA, AND RHEUMATOID ARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1057.3-1058
Author(s):  
E. Traianos ◽  
B. Dibnah ◽  
D. Lendrem ◽  
Y. Clark ◽  
V. Macrae ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Daytime Sleepiness ◽  
Vagus Nerve Stimulation ◽  
Chronic Fatigue ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Physical Fatigue ◽  
Fatigue Syndrome ◽  
Tnf Α ◽  
Patient Reported

Background:Fatigue is reported as a common symptom among autoimmune and other chronic diseases such as fibromyalgia (FM), a long-term condition with uncertain pathophysiology. Previous studies from our group suggest that non-invasive vagus nerve stimulation (nVNS) may contribute to the improvement of patient reported outcome measures (PROMs) of fatigue in patients with primary Sjögren’s Syndrome (1).Objectives:This follow-up study uses the gammaCore device (electroCore) to assess the effect of nVNS on PROMs of fatigue and immune responses in chronic fatigue syndrome (CFS), FM and rheumatoid arthritis (RA).Methods:The study included thirteen CFS, fourteen FM and fifteen RA patients who used the gammaCore nVNS device twice daily over a 26-day period. Pre- and post- nVNS bloods were drawn at baseline and final visits. Whole blood samples were stimulated with 2 ng/mL lipopolysaccharide (LPS) and the IL-6 and TNF-α cytokine concentrations were quantified at 24 hours. In addition, the epidermal growth factor (EGF), IFN-γ, IL-6, IP-10, MIP-1α, and TNF-α levels were measured in ‘pre-nVNS’ serum and flow cytometric profiles of whole blood immune cells were analysed. The patient reported outcome measures (PROMs) recorded at each visit were the Visual Analogue Scale (VAS) (0-100 cm) of abnormal fatigue, Hospital Anxiety and Depression (HAD) Scale, Orthostatic Grading Scale, Epworth Sleepiness Scale (daytime sleepiness), and Profile of fatigue (PRO-F) for Physical and Mental fatigue. Paired t-tests were performed to assess for changes in PROMs, cytokine levels, and cell subset distribution and associations of cytokine response with PROMs were investigated by correlation analyses.Results:Eleven CFS, twelve FM and fourteen RA patients completed the study. There was a significant reduction in daytime sleepiness in CFS (p =0.0321) and FM (p =0.0294) patients between the final and baseline visits and a significant reduction in HAD depression (p =0.0413) in FM (Fig.1). Improvement in VAS for abnormal fatigue, HAD-Anxiety, HAD-Depression, PRO-F Physical and Mental fatigue was observed in all three groups over the study period with a reduction in VAS fatigue in 64% of CFS, 67% of FM and 62% of RA patients. There were no significant changes in the immune cell subsets or in cytokine response. Finally, higher baseline pre-nVNS supernatant IL-6 levels were predictive of an improvement in VAS fatigue (p =0.0006), Daytime Sleepiness (p =0.0466) and PRO-F Physical fatigue (p =0.0196) in RA, while higher baseline TNF-α levels were predictive of an improvement in VAS fatigue (p =0.0003), Daytime Sleepiness (p =0.0380), Orthostatic (p =0.0281) and PRO-F Physical fatigue (p =0.0007) in FM.Conclusion:Our findings suggest that nVNS may contribute to the improvement of PROMs of fatigue in CFS, FM and RA. NVNS led to significant reductions in daytime sleepiness in CFS and FM, and depression in FM. Further studies and a larger sample size are needed to investigate the potential effects of nVNS on diseases characterised by persistent fatigue.References:[1]Tarn J, Legg S, Mitchell S, Simon B, Ng WF. The Effects of Noninvasive Vagus Nerve Stimulation on Fatigue and Immune Responses in Patients With Primary Sjögren’s Syndrome. Neuromodulation Technol Neural Interface. 2018;22(5):580–5.Figure 1.VAS for abnormal fatigue and PROMs recorded at baseline and final visits in patients with chronic fatigue syndrome (CFS), fibromyalgia (FM) and rheumatoid arthritis (RA). Boxplots show the median, upper, and lower quartiles for PROMs at visit 1 and visit 3 in each disease group. Paired-t tests revealed a significant reduction in daytime sleepiness in CFS and FM (B), and a significant reduction in HAD depression in FM (E). Improvement trends were observed in VAS for abnormal fatigue, HAD-Anxiety, HAD-Depression, PRO-F Physical fatigue and PRO-F Mental fatigue in all three groups over the 26-day study period.Acknowledgements:This study received infrastructural support from the National Institute of Health Research (NIHR) Newcastle Biomedical Research Centre at Newcastle Hospitals Foundation Trust and Newcastle University.Disclosure of Interests:Emmanuella Traianos: None declared, Bethany Dibnah: None declared, Dennis Lendrem: None declared, Yasmin Clark: None declared, Victoria Macrae: None declared, Victoria Slater: None declared, Karl Wood: None declared, David Storey: None declared, Bruce Simon Shareholder of: Bruce Simon is an employee and shareholder of electroCore., Employee of: electroCore, Inc., Justyna Blake Shareholder of: Justyna Blake is an employee of electroCore, and receives stock ownership., Employee of: electroCore, Inc., Jessica Tarn: None declared, Wan Fai Ng: None declared

scholarly journals Classification of Pain Dynamics in Sickle Cell Disease from Mobile App Reporting

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 983-983
Author(s):  
Gary Nave ◽  
Swati Padhee ◽  
Amanuel Alambo ◽  
Kumar Utkarsh ◽  
Tanvi Banerjee ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Research Funding ◽  
Class Ii ◽  
Mobile App ◽  
Class I ◽  
Class Iii ◽  
Cell Disease ◽  
Patient Reported ◽  
Pain Trajectories ◽  
Over Time

Abstract Background: Sickle Cell Disease (SCD) is a chronic blood disorder in which complications result from vaso-occlusion. Pain is the most common symptom reported in patients with SCD and includes both acute unpredictable pain as well as chronic pain. Chronic pain is clinically defined as having more days with pain than without pain over a period of 6 months. Various classifications systems have been developed to better understand pain phenotypes, however, there is variability in data and groupings of patients. Recent work based on patient-reported outcome data has shown that patients may be classified into three subgroups: infrequent acute pain, limited recent pain with moderate long-term pain, and persistent severe pain. An improved understanding of the ways in which pain dynamics manifest over time will allow patients and medical providers to better manage pain. Using previously-published data which collected self-reported data through a mobile app over 6 months (Clifton et al., 2017, J. Comput. Biol.), we aimed to characterize the different ways in which patients experienced pain over time. In this work, we sought to identify classes of patients based only on their self reported pain levels. Methods: Patients within the previously-published study were asked to self-report their pain levels from 0-10 on a daily basis through a mobile app. The study included 39 patients (16 male, 23 female), with a mean age of 33.4. Patients reported their pain an average of 0.4 times per day over an average of 164.6 days. To allow for the possibility that patients' experiences change over time, we windowed the time series of pain dynamics into non-overlapping two-week windows. Within each window, the data were linearly interpolated to regularly-spaced samples. Then, we applied spectral clustering to identify classes of similar pain trajectories within the windowed data. Within the data, we also identified patients with and without chronic pain within the sample based on whether or not they have taken long-acting opioid medications, which are commonly prescribed for those with a diagnosis of chronic pain. With this identification, we compared patients within the identified classes with patients diagnosed with chronic pain. Results: We found that three classes of pain dynamics may be identified from the data considered: class I, class II, and class III (Figure). Class I pain trajectories have mild baseline pain, typically 0, with acute exacerbations of low to medium pain levels. Class II trajectories show moderate mean pain values, and show large variation within each trajectory. Class III trajectories show consistently high pain levels, rarely dropping below 7. All three classes include patients who have been diagnosed with chronic pain, but the proportion of patients with chronic pain differs. Patients with chronic pain represented 32% of samples in class I, 83% of samples in class II, and 86% of samples in class III. Conclusions: Based only on self-reported pain over time, clustering pain experiences into classes yields three distinct classes. These classes do not perfectly align with chronic pain diagnoses, but classes II and III both contain mostly chronic pain patients. Based on this and the unique behaviors of those classes, it may be useful to differentiate chronic pain into persistent chronic pain and intermittent chronic pain. Moreover, the findings of these classes are similar to results found from analyzing patient reported outcomes and show promise for the continued use of mHealth apps to acquire patient reported symptoms. Figure 1 Figure 1. Disclosures Shah: CSL Behring: Consultancy; Emmaus: Consultancy; Novartis: Research Funding, Speakers Bureau; Bluebird Bio: Consultancy; Guidepoint Global: Consultancy; GLG: Consultancy; Alexion: Speakers Bureau; GBT: Consultancy, Research Funding, Speakers Bureau.

scholarly journals Patient‐reported outcome measures ( PROMs ): making sense of individual PROM scores and changes in PROM scores over time

Nephrology ◽  
2020 ◽  
Author(s):  
Esmee M. Willik ◽  
Caroline B. Terwee ◽  
Willem Jan W. Bos ◽  
Marc H. Hemmelder ◽  
Kitty J. Jager ◽  
...  
Keyword(s):  
Outcome Measures ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Making Sense ◽  
Patient Reported ◽  
Over Time

scholarly journals A Systematic Review of Patient-Reported Outcome Measures Assessing Body Image Disturbance in Patients with Head and Neck Cancer

2019 ◽  
Vol 160 (6) ◽  
pp. 941-954 ◽  
Author(s):  
Mark A. Ellis ◽  
Katherine R. Sterba ◽  
Emily A. Brennan ◽  
Stacey Maurer ◽  
Elizabeth G. Hill ◽  
...  
Keyword(s):  
Systematic Review ◽  
Body Image ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Treatment Strategies ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Body Image Disturbance ◽  
Patient Reported ◽  
Over Time

Objective To synthesize published literature describing the severity of body image disturbance (BID) in patients with head and neck cancer (HNC) over time, its psychosocial and functional associations, and treatment strategies as assessed by patient-reported outcome measures (PROMs). Data Source PubMed/MEDLINE, Scopus, PsycINFO, Web of Science, and Google Scholar. Review Methods A systematic review of the English-language literature was performed to identify studies of BID in patients with HNC using psychometrically validated PROMs to assess (1) severity of BID over time, (2) psychosocial and functional associations, and (3) management strategies. Results A total of 17 studies met inclusion criteria. BID was assessed via 10 different PROMs, none of which were HNC-specific measures of BID. Two of 2 longitudinal studies (100%) reported that BID improved from pretreatment to posttreatment, and 2 of 3 longitudinal studies (67%) showed that the severity of BID decreased over time as survivors got further out from treatment. Seven of 17 studies (41%) described negative functional and psychosocial associations with BID, although study methodology limited conclusions about cause and effect. None of the studies assessing interventions to manage BID (0/2, 0%) demonstrated an improvement in BID relative to control. Conclusion BID in patients with HNC has negative functional and psychosocial associations and lacks evidence-based treatment. Research is limited by the lack of an HNC-specific BID PROM. Further research should address knowledge gaps related to the lack of an HNC-specific BID PROM, longitudinal course of BID in patients with HNC, confusion with regards to risk factors and outcomes, and lack of prevention and treatment strategies.

Real-world implementation of serious illness care program (SICP) in cancer care.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e24112-e24112
Author(s):  
Safiya Karim ◽  
Sasha M. Lupichuk ◽  
Amy Tan ◽  
Aynharan Sinnarajah ◽  
Jessica Simon
Keyword(s):  
Real World ◽  
Care Program ◽  
Patient Reported Outcome ◽  
Comfort Level ◽  
Healthcare Team ◽  
Serious Illness ◽  
The Real ◽  
Patient Reported ◽  
Seriously Ill Patients ◽  
Over Time

e24112 Background: The Serious Illness Care Program (SICP) is a system-based intervention, including a conversation guide, which facilitates improved advance care planning (ACP) conversations between clinicians and seriously ill patients. A recent randomized control trial found the program reduced symptoms of depression and anxiety amongst oncology out-patients and improved process outcomes. We implemented the SICP in our center to determine if the effects of this program could be translated into the real world. Methods: Two outpatient oncology clinics implemented the SICP, each over a 16-week period. Patients were identified based on an answer of “no” to the question “would I be surprised if this patient died within the next year?”, or any patient with a diagnosis of metastatic pancreatic cancer, or symptom scores of > 7 on more than three categories of the patient reported outcome dashboard. Physicians were trained on how to conduct the SICP conversation. One patient per week was identified and prepared to have the SICP conversation with the goal of at least 12 conversations in each 16-week period. Rates of SICP conversation documentation on our system’s “ACP and goals of care designation (GCD) Tracking Record” and GCD orders were recorded. Patient satisfaction after each conversation and physician comfort level over time were assessed. Results: 16 patients were identified (8 patients in each 16-week period). One patient was lost to follow-up. Of the remaining 15 patients who had the SICP conversation, 14 (93%) had documentation on the Tracking Record and 8 (53%) had a GCD order. This was a major improvement over baseline rates of documentation (e.g. < 1 % Tracking Record use and 16% GCD for patients with GI cancers). 14 patients completed satisfaction surveys, of which 12 (86%) felt “completely” or “quite a bit” more heard or understood. Physician comfort level increased from 3.6 to 4.8 and from 4.8 to 5 out of 5, respectively over each 16-week period. Conclusions: SICP implementation resulted in high rates of documentation of goals and preferences. Patients felt heard and understood by their healthcare team, and comfort in these conversations improved over time for physicians. The goal number of conversations was not met, but otherwise the SICP was feasible to implement in the real world. Further study is required to identify the appropriate triggers and barriers to routine SICP conversations.

scholarly journals Changes in pulmonary function and patient-reported outcomes during COVID-19 recovery: a longitudinal, prospective cohort study

2021 ◽  
pp. 00243-2021
Author(s):  
Aditi S. Shah ◽  
Min Hyung Ryu ◽  
Cameron J Hague ◽  
Darra T. Murphy ◽  
James C. Johnston ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Symptom Onset ◽  
Prospective Cohort ◽  
Pulmonary Function Tests ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Considerable Proportion ◽  
Patient Reported ◽  
Underlying Mechanisms ◽  
Over Time

ObjectivesTo compare respiratory and patient-reported outcome measures (PROMs) between 3 and 6 months after symptom onset and to identify features that predict these changes.MethodsThis is a consecutive prospective cohort of 73 patients who were hospitalised with COVID-19. We evaluated the changes in pulmonary function tests (PFTs) and PROMs between 3 and 6 months and then investigated the associations between outcomes (change in diffusing capacity for carbon monoxide of the lung (DLCO), dyspnea, and quality of life (QOL)) and clinical and radiological features.ResultsThere was improvement in forced vital capacity (FVC), total lung capacity (TLC), and DLCO between 3 and 6 months by 3.25%, 3.82% and 5.69% respectively; however, there was no difference in PROMs. Reticulation and total CT scores were associated with lower DLCO %-predicted at 6 months (coefficients; −8.7 and −5.3 respectively). The association between radiological scores and DLCO were modified by time, with the degree of association between ground glass and DLCO having decreased markedly over time. There was no association between other predictors and change in dyspnea or QOL over time.ConclusionsThere is improvement in pulmonary function measurements between 3 and 6 months after COVID-19 symptom onset; however, PROMs did not improve. A higher reticulation and total CT score are negatively associated with DLCO, but this association is attenuated over time. Lastly, there is a considerable proportion of patients with unexplained dyspnea at 6 months, motivating further research to identify the underlying mechanisms.

scholarly journals A Mobile App for Measuring Real Time Fatigue in Patients with Multiple Sclerosis: Introducing the Fimo Health App

2021 ◽  
Vol 11 (9) ◽  
pp. 1235
Author(s):  
Jana Mäcken ◽  
Marie Wiegand ◽  
Mathias Müller ◽  
Alexander Krawinkel ◽  
Michael Linnebank
Keyword(s):  
Influencing Factors ◽  
Transtheoretical Model ◽  
Treatment Guidelines ◽  
Behavioral Therapy ◽  
Holistic Approach ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Current Treatment ◽  
Mobile App ◽  
Patient Reported

Although fatigue is one of the most disabling symptoms of MS, its pathogenesis is not well understood yet. This study aims to introduce a new holistic approach to measure fatigue and its influencing factors via a mobile app. Fatigue is measured with different patient-reported outcome measures (Visual Analog Scale, Fatigue Severity Scale) and tests (Symbol Digit Modalities Test). The influencing vital and environmental factors are captured with a smartwatch and phone sensors. Patients can track these factors within the app. To individually counteract their fatigue, a fatigue course, based on the current treatment guidelines, was implemented. The course implies knowledge about fatigue and MS, exercises, energy-conservation management, and cognitive behavioral therapy. Based on the Transtheoretical Model of Behavior Change, the design of the Fimo health app follows the ten strategies of the process of change, which is a proven approach to designing health intervention programs. By monitoring fatigue and individual influencing factors, patients can better understand and manage their fatigue. They can share their data and insights about fatigue and its influencing factors with their doctors. Thus, they can receive individualized therapies and drug plans.

scholarly journals Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study

10.2196/14665 ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. e14665 ◽  
Author(s):  
William Benjamin Nowell ◽  
Jeffrey R Curtis ◽  
Sandra K Nolot ◽  
David Curtis ◽  
Shilpa Venkatachalam ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Digital Technology ◽  
Subjective Experience ◽  
Patient Reported Outcome ◽  
Mobile App ◽  
Smartphone App ◽  
Activity Data ◽  
Physician Visits ◽  
Study Results ◽  
Patient Reported

Background Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passive collection of patients’ biometric and activity data. If it is shown to be strongly related to data captured by patient-reported outcome (PRO) measures, information collected passively from wearable digital technology could serve as an objective proxy or be complementary to patients’ subjective experience of RA symptoms. Objective The goal of this study is to characterize the extent to which digital measures collected from a consumer-grade smartwatch agree with measures of RA disease activity and other PROs collected via a smartphone app. Methods This observational study will last 6 months for each participant. We aim to recruit 250 members of the ArthritisPower registry with an RA diagnosis who will receive a smartwatch to wear for the period of the study. From the ArthritisPower mobile app on their own smartphone device, participants will be prompted to answer daily and weekly electronic PRO (ePRO) measures for the first 3 months. Results The study was launched in December 2018 and will require up to 18 months to complete. Study results are expected to be published by the end of 2021. Conclusions The completion of this study will provide important data regarding the following: (1) the relationship between passively collected digital measures related to activity, heart rate, and sleep collected from a smartwatch with ePROs related to pain, fatigue, physical function, and RA flare entered via smartphone app; (2) determine predictors of adherence with smartwatch and smartphone app technology; and (3) assess the effect of study-specific reminders on adherence with the smartwatch. International Registered Report Identifier (IRRID) DERR1-10.2196/14665

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