Negotiating the coresearcher mandate – service users’ experiences of doing collaborative research on mental health

Moltu, Christian; Stefansen, Jon; Svisdahl, Marit; Veseth, Marius

doi:10.3109/09638288.2012.656792

Cited by 1,064 publications

(107 citation statements)

References 22 publications

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“…First, 3 drug abuse researchers or “domain experts” (RD, FL, RC) conducted preliminary “open” coding [40] of several batches of 200-300 tweets to develop and refine the coding rules for source (Multimedia Appendix 1) and sentiment classification (Multimedia Appendix 2). Next, to assess intercoder reliability, a random subsample of 300 tweets was selected from a batch of 3000 tweets that were randomly extracted from eDrugTrends database of tweets collected between May and July of 2016.…”

Section: Methodsmentioning

confidence: 99%

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Daniulaityte¹,

Chen²,

Lamy³

et al. 2016

JMIR Public Health Surveill

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BackgroundTo harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content.ObjectivesThe objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid–related tweets.MethodsTweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant.ResultsIn multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, ...

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Section: Methodsmentioning

confidence: 99%

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Daniulaityte¹,

Chen²,

Lamy³

et al. 2016

JMIR Public Health Surveill

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“…Comprehensive care for CKP due to knee OA is also more effective at reducing pain in the long-term compared with physical therapy only [13-16]. However, chronic pain programs are rare for CKP, and over 80% of individuals with CKP due to knee OA receive suboptimal conservative care [17]. Furthermore, CKP patients show poor adherence to existing treatments [18].…”

Section: Introductionmentioning

confidence: 99%

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program

Smittenaar¹,

Erhart‐Hledik²,

Kinsella³

et al. 2017

JMIR Rehabil Assist Technol

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BackgroundChronic knee pain (CKP) affects a large number of adults, many of whom do not receive best-practice care and are at high risk for unnecessary surgery.ObjectiveThe aim of this study was to investigate the effect of the Hinge Health 12-week digital care program (DCP) for CKP on knee pain and function, with secondary outcomes of surgery interest and satisfaction, at 12 weeks and 6 months after starting the program.MethodsIndividuals with CKP were recruited onto the 12-week program, comprising sensor-guided physical exercises, weekly education, activity tracking, and psychosocial support such as personal coaching and cognitive behavioral therapy (CBT). We used a single-arm design with assessment of outcomes at baseline, 12 weeks, and 6 months after starting the program. We used a linear mixed effects model with Tukey contrasts to compare timepoints and report intention-to-treat statistics with last observation carried forward.ResultsThe cohort consisted of 41 individuals (32 female, mean age 52 years, SD 9 years). Between baseline and week 12, participants reported clinically significant improvements in the Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS) function scales of 16 points (95% CI 12-21, P<.001) and 10 points (95% CI 6-14, P<.001), respectively. Significant reductions of 57% (mean difference 30, 95% CI 21-38, P<.001) and 51% (mean difference 25, 95% CI 16-33, P<.001) in visual analog scale (VAS) knee pain and stiffness, respectively, were observed at 12 weeks, as well as a 67% reduction in surgery interest (mean reduction 2.3 out of 10, 95% CI 1.5-3.1, P<.001). Average satisfaction at week 12 was 9.2 out of 10. Critically, all improvements were maintained at 6 months at similar or greater magnitude.ConclusionsParticipants on the Hinge Health DCP for CKP showed substantial clinical improvements that were maintained 6 months after enrolling in the program. This shows that DCPs carry strong potential to deliver evidence-based, cost-effective care to those suffering from CKP.

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“…Qualitative data were collected from open-ended questions in questionnaires and from CM reports, including their daily activities and actions [73-75]. …”

Section: Methodsmentioning

confidence: 99%

Young People, Adult Worries: Randomized Controlled Trial and Feasibility Study of the Internet-Based Self-Support Method “Feel the ViBe” for Adolescents and Young Adults Exposed to Family Violence

Rosmalen-Nooijens¹,

S²,

Prins³

et al. 2017

J Med Internet Res

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BackgroundAdolescents and young adults (AYAs) are of special interest in a group of children exposed to family violence (FV). Past-year prevalence of exposure to FV is known to be highest in AYAs and has severe consequences. Peer support is an effective approach to behavior change and the Internet is considered suitable as a mode of delivery.ObjectiveThe study aimed to evaluate both effectiveness and feasibility of a randomized controlled trial (RCT) and feasibility study of the Internet-based self-support method “Feel the ViBe” (FtV) using mixed-methods approach to fully understand the strengths and weaknesses of a new intervention.MethodsAYAs aged 12-25 years and exposed to FV were randomized in an intervention group (access to FtV + usual care) and a control group (minimally enhanced usual care) after they self-registered themselves. From June 2012 to July 2014, participants completed the Impact of Event Scale (IES) and Depression (DEP) and Anxiety (ANX) subscales of the Symptom CheckList-90-R (SCL-90) every 6 weeks. The Web Evaluation Questionnaire was completed after 12 weeks. Quantitative usage data were collected using Google analytics and content management system (CMS) logs and data files. A univariate analysis of variance (UNIANOVA) and mixed model analysis (intention-to-treat [ITT], complete case) were used to compare groups. Pre-post t tests were used to find within-group effects. Feasibility measures structurally address the findings. The CONsolidated Standards Of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth (CONSORT-EHEALTH) checklist was closely followed.ResultsIn total, 31 out of 46 participants in the intervention group and 26 out of 47 participants in the control group started FtV. Seventeen participants (intervention: n=8, control: n=9) completed all questionnaires. Mixed model analysis showed significant differences between groups on the SCL-90 DEP (P=.04) and ANX (P=.049) subscales between 6 and 12 weeks after participation started. UNIANOVA showed no significant differences. Pre-post paired sample t tests showed significant improvements after 12 weeks for the SCL-90 DEP (P=.03) and ANX (P=.046) subscales. Reported mean Web-based time per week was 2.83 with a session time of 36 min. FtV was rated a mean 7.47 (1-10 Likert scale) with a helpfulness score of 3.16 (1-5 Likert scale). All participants felt safe. Two-thirds of the intervention participants started regular health care.ConclusionsNo changes on the IES were found. SCL-90 DEP and ANX showed promising results; however, the calculated sample size was not reached (n=18). FtV functions best as a first step for adolescents and young adults in an early stage of change. FtV can be easily implemented without extensive resources and fits best in the field of public health care or national governmental care.Trial RegistrationNetherlands National Trial Register (NTR): NTR3692; http://www.trialregister.nl/trialreg/admin/ rctview.asp?TC=3692 (Archived by WebCite at http://www.webcitation.org/6qIeKyjA4)

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Negotiating the coresearcher mandate – service users’ experiences of doing collaborative research on mental health

Abstract: Our findings generate hypotheses on how participatory research into mental health issues can be fruitfully organized, in a way that empowers service users to active and constructive participation.

Cited by 1,064 publications

References 22 publications

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program

Young People, Adult Worries: Randomized Controlled Trial and Feasibility Study of the Internet-Based Self-Support Method “Feel the ViBe” for Adolescents and Young Adults Exposed to Family Violence

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