Abstract

Background Axial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the spine and sacroiliac joints, characterised by fluctuating periods of flare and remission. Management is based on patient-reported symptoms and outcome measures collected at follow-up appointments which may be subject to recall bias. Smartphone technologies for monitoring disease symptoms provide an opportunity to gain a more complete understanding of disease burden and symptom patterns and may facilitate optimisation and personalisation of axSpA management. Methods Patients with axSpA attending the Royal National Hospital for Rheumatic Diseases in Bath were invited to participate in the study. Through the uMotif symptom tracking app, patients were sent daily reminders to log pain, fatigue, sleep, recommended exercise, mood and stress using 5-point Likert scales (higher scores indicating better outcomes eg. No pain=5), in addition to optional variables such as screen time, caffeine intake and menstrual cycle. For each patient, at each time-point, Spearman rank correlation coefficients were used to evaluate inter-variable correlations. Lagged variables were used to assess correlations with variables from the previous day (variable-1). Results Between 05/04/18 and 08/03/19, 174 patients consented for research and logged a mean of 99.73 (SD = 99.97) days of data (range=1 - 323 days). Significant correlations were identified between uMotif variables (Table 1), including pain and fatigue; mood and stress; hot flushes and fatigue/pain; confidence in self-management and mood/pain; smoking and pain/fatigue; exercise and sleep; exercise and mood - supporting existing evidence regarding exercise implementation in axSpA. Table 1 Spearman Rank correlations of uMotif variables Pain Fatigue Sleep Quality Recom mended Exercise Mood Stress Pain (N = 171) 1.000 0.526 *** 0.395 *** 0.098 *** 0.423 *** 0.325 *** Fatigue (N = 170) 1.000 0.435 *** 0.097 *** 0.441 *** 0.378 *** Sleep Quality (N = 172) 1.000 0.135 *** 0.411 *** 0.253 *** Recommended Exercise (N = 170) 1.000 0.182 *** 0.128 *** Mood (N = 170) 1.000 0.521 *** Stress (N = 169) 1.000 Chest pain (N = 30) 0.428 *** 0.324 *** 0.229 *** -0.082 *** 0.188 *** 0.242 *** Screen time (N = 25) 0.270 *** 0.248 *** 0.038 -0.069 ** 0.307 *** 0.384 *** Caffeine intake (N = 67) -0.040 *** 0.075 *** 0.012 0.128 *** -0.044 *** -0.029 * Eyesight (N = 29) 0.289 *** 0.235 *** 0.377 *** 0.193 *** 0.460 *** 0.189 *** Hydration (N = 56) 0.070 *** 0.109 *** -0.029 * 0.183 *** 0.092 *** 0.048 *** Hot flushes (N = 18) 0.518 *** 0.555 *** 0.384 *** 0.063 *** 0.386 *** 0.202 *** Confidence in self- management (N = 41) 0.503 *** 0.372 *** 0.336 *** 0.110 *** 0.523 *** 0.370 *** Menstrual cycle (N = 14) 0.130 *** 0.098 *** 0.045 -0.002 0.100 *** 0.109 *** Smoking today (N = 5) 0.394 ** 0.436 *** 0.211 -0.012 0.151 -0.282 * Pain -1 0.719 *** 0.459 *** 0.354 *** 0.095 *** 0.371 *** 0.276 *** Fatigue -1 0.454 *** 0.676 *** 0.323 *** 0.074 *** 0.337 *** 0.286 *** Sleep Quality -1 0.353 *** 0.342 *** 0.576 *** 0.123 *** 0.336 *** 0.192 *** Recommended Exercise -1 0.094 *** 0.071 *** 0.106 *** 0.587 *** 0.134 *** 0.093 *** Mood -1 0.376 *** 0.339 *** 0.338 *** 0.129 *** 0.647 *** 0.360 *** Stress -1 0.289 *** 0.309 *** 0.200 *** 0.100 *** 0.361 *** 0.555 *** Chest pain -1 0.389 *** 0.307 *** 0.228 *** -0.072 ** 0.112 *** 0.154 *** Screen time -1 0.275 *** 0.218 *** 0.018 -0.101 *** 0.211 *** 0.292 *** Caffeine intake -1 -0.037 ** 0.102 *** 0.024 0.116 *** -0.031 * -0.028 * Eyesight -1 0.296 *** 0.202 *** 0.347 *** 0.184 *** 0.439 *** 0.136 *** Hydration -1 0.102 *** 0.161 *** -0.020 0.121 *** 0.100 *** 0.066 *** Hot flushes -1 0.481 *** 0.527 *** 0.343 *** 0.088 *** 0.338 *** 0.116 *** Confidence in self- management -1 0.460 *** 0.342 *** 0.298 *** 0.069 *** 0.437 *** 0.297 *** Menstrual cycle -1 0.043 0.033 0.050 -0.041 0.048 0.033 Smoking today -1 0.456 0.522 * 0.023 0.130 -0.036 -0.419 * p < 0.05, ** p < 0.01, *** p < 0.001; N=number of patients Conclusion These findings demonstrate relationships between a variety of patient-reported symptoms, including variables that to our knowledge, have not yet been explored in axSpA. In future research, it will be important to determine whether there is a chronological pattern within an individual or combination of variables that could predict a flare. Greater understanding of axSpA disease patterns and identification of the optimal timing of intervention to ameliorate these symptoms may ultimately reduce flare frequency, duration and intensity, and greatly improve quality of life for patients with axSpA.
Original languageEnglish
DOIs
Publication statusPublished - Apr 2020

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