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Purpose Treatment with the tyrosine kinase inhibitor (TKI) imatinib in patients with gastrointestinal stromal tumours (GIST) causes symptoms that could negatively impact health-related quality of life (HRQoL). Treatment-related symptoms are usually clinician-reported and little is known about patient reports. We used survey and online patient forum data to investigate (1) prevalence of patient-reported symptoms; (2) coverage of symptoms mentioned on the forum by existing HRQoL questionnaires; and (3) priorities of prevalent symptoms in HRQoL assessment. Methods In the cross-sectional population-based survey study, Dutch GIST patients completed items from the EORTC QLQ-C30 and Symptom-Based Questionnaire (SBQ). In the forum study, machine learning algorithms were used to extract TKI side-effects from English messages on an international online forum for GIST patients. Prevalence of symptoms related to imatinib treatment in both sources was calculated and exploratively compared. Results Fatigue and muscle pain or cramps were reported most frequently. Seven out of 10 most reported symptoms (i.e. fatigue, muscle pain or cramps, facial swelling, joint pain, skin problems, diarrhoea, and oedema) overlapped between the two sources. Alopecia was frequently mentioned on the forum, but not in the survey. Four out of 10 most reported symptoms on the online forum are covered by the EORTC QLQ-C30. The EORTC-SBQ and EORTC Item Library cover 9 and 10 symptoms, respectively. Conclusion This first overview of patient-reported imatinib-related symptoms from two data sources helps to determine coverage of items in existing questionnaires, and prioritize HRQoL issues. Combining cancer-generic instruments with treatment-specific item lists will improve future HRQoL assessment in care and research in GIST patients using TKI.

Background Increasingly, social media is being recognized as a potential resource for patient-generated health data, for example, for pharmacovigilance. Although the representativeness of the web-based patient population is often noted as a concern, studies in this field are limited. Objective This study aimed to investigate the sample bias of patient-centered social media in Dutch patients with gastrointestinal stromal tumor (GIST). Methods A population-based survey was conducted in the Netherlands among 328 patients with GIST diagnosed 2-13 years ago to investigate their digital communication use with fellow patients. A logistic regression analysis was used to analyze clinical and demographic differences between forum users and nonusers. Results Overall, 17.9% (59/328) of survey respondents reported having contact with fellow patients via social media. Moreover, 78% (46/59) of forum users made use of GIST patient forums. We found no statistically significant differences for age, sex, socioeconomic status, and time since diagnosis between forum users (n=46) and nonusers (n=273). Patient forum users did differ significantly in (self-reported) treatment phase from nonusers (P=.001). Of the 46 forum users, only 2 (4%) were cured and not being monitored; 3 (7%) were on adjuvant, curative treatment; 19 (41%) were being monitored after adjuvant treatment; and 22 (48%) were on palliative treatment. In contrast, of the 273 patients who did not use disease-specific forums to communicate with fellow patients, 56 (20.5%) were cured and not being monitored, 31 (11.3%) were on curative treatment, 139 (50.9%) were being monitored after treatment, and 42 (15.3%) were on palliative treatment. The odds of being on a patient forum were 2.8 times as high for a patient who is being monitored compared with a patient that is considered cured. The odds of being on a patient forum were 1.9 times as high for patients who were on curative (adjuvant) treatment and 10 times as high for patients who were in the palliative phase compared with patients who were considered cured. Forum users also reported a lower level of social functioning (84.8 out of 100) than nonusers (93.8 out of 100; P=.008). Conclusions Forum users showed no particular bias on the most important demographic variables of age, sex, socioeconomic status, and time since diagnosis. This may reflect the narrowing digital divide. Overrepresentation and underrepresentation of patients with GIST in different treatment phases on social media should be taken into account when sourcing patient forums for patient-generated health data. A further investigation of the sample bias in other web-based patient populations is warranted.

Patients advise their peers on how to cope with their illness in daily life on online support groups. To date, no efforts have been made to automatically extract recommended coping strategies from online patient discussion groups. We introduce this new task, which poses a number of challenges including complex, long entities, a large long-tailed label space, and cross-document relations. We present an initial ontology for coping strategies as a starting point for future research on coping strategies, and the first end-to-end pipeline for extracting coping strategies for side effects. We also compared two possible computational solutions for this novel and highly challenging task; multi-label classification and named entity recognition (NER) with entity linking (EL). We evaluated our methods on the discussion forum from the Facebook group of the worldwide patient support organization 'GIST support international' (GSI); GIST support international donated the data to us. We found that coping strategy extraction is difficult and both methods attain limited performance (measured with F1 score) on held out test sets; multi-label classification outperforms NER+EL (F1=0.220 vs F1=0.155). An inspection of the multi-label classification output revealed that for some of the incorrect predictions, the reference label is close to the predicted label in the ontology (e.g. the predicted label 'juice' instead of the more specific reference label 'grapefruit juice'). Performance increased to F1=0.498 when we evaluated at a coarser level of the ontology. We conclude that our pipeline can be used in a semi-automatic setting, in interaction with domain experts to discover coping strategies for side effects from a patient forum. For example, we found that patients recommend ginger tea for nausea and magnesium and potassium supplements for cramps.This information can be used as input for patient surveys or clinical studies.

PURPOSE: The aim of this study was to determine changes in physical activity, nutrition, sleep behaviour and body composition in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application (app).METHODS: A 12-week pre-post intervention study was performed, starting with a 1-week control period. Physical activity and sleep behaviour were continuously measured with a Fitbit charge 3. Self-reported nutritional intake, body mass and waist circumference were collected. Pre-post outcomes were compared with a paired-sample t-test or Wilcoxon signed-rank test. Fitbit data were analysed with a mixed model or a panel linear model. Effect sizes were determined and significance was accepted at p < .05. RESULTS: Thirty participants completed the study. No significant changes in physical activity (+1.5 √steps) and sleep quality (-9.7 sleep minutes; -1.2% sleep efficiency) were found. Significant reduction in energy (-1022 kJ, d = 0.71), protein (-8.3 g, d = 0.61) and fat (-13.1 g, d = 0.87) intake, body mass (-2.2 kg, d = 0.61) and waist circumference (-3.3 cm, d = 0.80) were found. CONCLUSION: Positive changes were found in nutritional behaviour and body composition, but not in physical activity and sleep quality. The WHEELS app seems to partly support healthy lifestyle behaviour.Implications for RehabilitationHealthy lifestyle promotion is crucial, especially for wheelchair users as they tend to show poorer lifestyle behaviour despite an increased risk of obesity and comorbidity.The WHEELS lifestyle app seems to be a valuable tool to support healthy nutrition choices and weight loss and to improve body satisfaction, mental health and vitality.

BACKGROUND Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions. OBJECTIVE We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic. METHODS In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data. RESULTS The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. CONCLUSIONS We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. CLINICALTRIAL ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735

Background Heart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique. Objective The objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level. Methods The HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise. Results With an overall MAPEall lesions of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPEno SCI=8.09%; MAPET1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPErest=6.5%, MAPEactivity=12.97%, and MAPEstrength=14.2%). Conclusions HR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution.