Remote sampling of biomarkers of inflammation with linked patient generated health data in patients with rheumatic and musculoskeletal diseases: an Ecological Momentary Assessment feasibility study.

BACKGROUND: People with rheumatic diseases experience troublesome fluctuations in fatigue. Debated causes include pain, mood and inflammation. To determine the relationships between these potential causes, serial assessments are required but are methodologically challenging. This mobile health (mHealth) study explored the viability of using a smartphone app to collect patient-reported symptoms with contemporaneous Dried Blood Spot Sampling (DBSS) for inflammation. METHODS: Over 30 days, thirty-eight participants (12 RA, 13 OA, and 13 FM) used uMotif, a smartphone app, to report fatigue, pain and mood, on 5-point ordinal scales, twice daily. Daily DBSS, from which C-reactive Protein (CRP) values were extracted, were completed on days 1-7, 14 and 30. Participant engagement was determined based on frequency of data entry and ability to calculate within- and between-day symptom changes. DBSS feasibility and engagement was determined based on the proportion of samples returned and usable for extraction, and the number of days between which between-day changes in CRP which could be calculated (days 1-7). RESULTS: Fatigue was reported at least once on 1085/1140 days (95.2%). Approximately 65% of within- and between-day fatigue changes could be calculated. Rates were similar for pain and mood. A total of 287/342 (83.9%) DBSS, were returned, and all samples were viable for CRP extraction. Fatigue, pain and mood varied considerably, but clinically meaningful (≥ 5 mg/L) CRP changes were uncommon. CONCLUSIONS: Embedding DBSS in mHealth studies will enable researchers to obtain serial symptom assessments with matched biological samples. This provides exciting opportunities to address hitherto unanswerable questions, such as elucidating the mechanisms of fatigue fluctuations.

Smoking and Gambling Disorder: Does Tobacco Use Influence Treatment Outcome?

The purpose of this study was to verify whether tobacco use influenced treatment outcome in a population of treatment seeking individuals with gambling disorder. Gambling disorder is defined as persistent and maladaptive gambling behaviour which meets four or more outlined criteria in the DSM-5. Tobacco use is the most frequent comorbidity with gambling disorder. A total of 676 treatment seeking individuals with gambling disorder were assessed at the National Problem Gambling Clinic in London. We analysed differences in socio-demographic, clinical and gambling variables between smokers and non-smokers and the relation between smoking behaviour and treatment completion and outcome. 46.4% (314) of our sample were daily tobacco users and were significantly younger, less likely to be in a stable relationship, more likely to be unemployed and have a lower education level. They were also significantly more likely to score higher on the AUDIT-C score and were significantly more likely to have used drugs in the last 30 days. There was no significant difference in PGSI score between smokers and non-smokers. We found that tobacco smokers did not have higher PGSI scores than non-smokers. Moreover, there was no significant difference between tobacco users and nonusers in terms of treatment completion and treatment outcome.