Randomized controlled trials and real-world data: differences and similarities to untangle literature data

Monti, Sara; Grosso, V.; Todoerti, Monica; Caporali, Roberto

doi:10.1093/rheumatology/key109

Cited by 112 publications

(90 citation statements)

References 20 publications

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“…Many other commercial and academic groups are developing smartphone apps for a wide variety of conditions that go beyond the simple direct-to-consumer market by seeking regulatory approval for their use in clinical contexts [17-19]. Unfortunately, not enough time has elapsed since the introduction of smartphone-based intervention apps to provide insight into their long-term repeated effects as well as their effects in real-world settings (ie, outside of clinical trials) [20-22].…”

Section: Introductionmentioning

confidence: 99%

Association Between Improvement in Baseline Mood and Long-Term Use of a Mindfulness and Meditation App: Observational Study

Athanas¹,

McCorrison²,

Smalley³

et al. 2019

JMIR Ment Health

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BackgroundThe use of smartphone apps to monitor and deliver health care guidance and interventions has received considerable attention recently, particularly with regard to behavioral disorders, stress relief, negative emotional state, and poor mood in general. Unfortunately, there is little research investigating the long-term and repeated effects of apps meant to impact mood and emotional state.ObjectiveWe aimed to investigate the effects of both immediate point-of-intervention and long-term use (ie, at least 10 engagements) of a guided meditation and mindfulness smartphone app on users’ emotional states. Data were collected from users of a mobile phone app developed by the company Stop, Breathe & Think (SBT) for achieving emotional wellness. To explore the long-term effects, we assessed changes in the users’ basal emotional state before they completed an activity (eg, a guided meditation). We also assessed the immediate effects of the app on users’ emotional states from preactivity to postactivity.MethodsThe SBT app collects information on the emotional state of the user before and after engagement in one or several mediation and mindfulness activities. These activities are recommended and provided by the app based on user input. We considered data on over 120,000 users of the app who collectively engaged in over 5.5 million sessions with the app during an approximate 2-year period. We focused our analysis on users who had at least 10 engagements with the app over an average of 6 months. We explored the changes in the emotional well-being of individuals with different emotional states at the time of their initial engagement with the app using mixed-effects models. In the process, we compared 2 different methods of classifying emotional states: (1) an expert-defined a priori mood classification and (2) an empirically driven cluster-based classification.ResultsWe found that among long-term users of the app, there was an association between the length of use and a positive change in basal emotional state (4% positive mood increase on a 2-point scale every 10 sessions). We also found that individuals who were anxious or depressed tended to have a favorable long-term emotional transition (eg, from a sad emotional state to a happier emotional state) after using the app for an extended period (the odds ratio for achieving a positive emotional state was 3.2 and 6.2 for anxious and depressed individuals, respectively, compared with users with fewer sessions).ConclusionsOur analyses provide evidence for an association between both immediate and long-term use of an app providing guided meditations and improvements in the emotional state.

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Section: Introductionmentioning

confidence: 99%

Association Between Improvement in Baseline Mood and Long-Term Use of a Mindfulness and Meditation App: Observational Study

Athanas¹,

McCorrison²,

Smalley³

et al. 2019

JMIR Ment Health

View full text Add to dashboard Cite

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“…With the increasing amount of information collected by registries, Electronic Health Records and the increasing use of sensors collecting in real time patients’ data, clinical research must evolve to take advantage of these new sources of information and implement them in routine practice 2 32. Given the exhaustive nature of clinical big data, they could be particularly interesting in the future to study rare diseases, rare outcomes and evaluate the efficacy of treatments in non-selected populations, which are difficult to assess in usual clinical trials 2 33. Omics is a growing field, particularly promising for personalised medicine as it supports the discovery of predictive biomarkers and therapeutic targets 3 4 34.…”

Section: Discussionmentioning

confidence: 99%

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations

et al. 2019

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ObjectiveTo assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).MethodsA systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.ResultsOf 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).ConclusionsBig data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

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“…П р о г р е с с в р е в м а т о л о г и и в X X I в е к е Открытые исследования и регистры РПКИ занимают центральное место в медицине, основанной на доказательствах [96][97][98], однако использование «жестких» критериев «включения» и «исключения» затрудняет генерализацию полученных данных на всю популяцию пациентов, наблюдаемых в клинической практике. Эта проблема получила название «брешь эффективностирезультативности» (efficacy-effectiveness gap) [99], которая в определенной степени может быть преодолена материалами регистров пациентов, позволяющими составить более полное представление об истинном месте лекарственных препаратов [100][101][102][103][104], включая ТОФА [105], в реальной клинической практике.…”

Section: таблицаunclassified

Efficacy and safety of tofacitinib for immune-mediated inflammatory rheumatic diseases (Part I)

Насонов

Авдеева²,

Лила

2020

Naučno-praktičeskaâ revmatologiâ

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Расшифровка механизмов патогенеза иммуновоспалительных ревматических заболеваний (ИВРЗ), основными формами кото-рых являются ревматоидный артрит (РА), спондилоартриты (СпА), включающие анкилозирующий спондилит (АС) и псориатиче-П р о г р е с с в р е в м а т о л о г и и в X X I в е к е Насонов Е.Л.научный руководитель ФГБНУ «НИИР им. В.А. Насоновой», академик РАН, профессор, докт. мед. наук 1 ФГБНУ «Научно-исследовательский институт ревматологии им. В.А. Насоновой»,

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Randomized controlled trials and real-world data: differences and similarities to untangle literature data

Cited by 112 publications

References 20 publications

Association Between Improvement in Baseline Mood and Long-Term Use of a Mindfulness and Meditation App: Observational Study

Association Between Improvement in Baseline Mood and Long-Term Use of a Mindfulness and Meditation App: Observational Study

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations

Efficacy and safety of tofacitinib for immune-mediated inflammatory rheumatic diseases (Part I)

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