Using feedback through digital technology to disrupt and change habitual behavior: A critical review of current literature

Hermsen, Sander; Frost, Jeana; Renes, Reint Jan; Kerkhof, P.C.M. van de

doi:10.1016/j.chb.2015.12.023

Cited by 164 publications

(130 citation statements)

References 98 publications

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“…For instance, feedback about students' performance is known to improve their study results (Hattie & Timperley, 2007). A recent review concluded that the effectiveness of technology-mediated feedback interventions has not yet been sufficiently examined (Hermsen, Frost, Renes, & Kerkhof, 2016), but more traditional feedback interventions have been extensively evaluated. In a review, Kluger and Denisi (1996) found that the majority of feedback interventions were effective, although it should be noted that for about one third of the interventions the effect was adverse rather than beneficial.…”

Section: Self-monitoring and Feedback Interventions Versus Pimentioning

confidence: 99%

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Dijk

Westerink

Beute

et al. 2017

Human–Computer Interaction

117

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Personal informatics (PI) systems allow users to collect and review personally relevant information. The purpose commonly envisioned for these systems is that they provide users with actionable, data-driven selfinsight to help them change their behavioral patterns for the better. Here, we review relevant theory as well as empirical evidence for this selfimprovement hypothesis. From a corpus of 6,568, only 24 studies met the selection criteria of being a peer-reviewed empirical study reporting on actionable, data-driven insights from PI data, using a "clean" PI system with no other intervention techniques (e.g., additional coaching) on a nonclinical population. First results are promising-many of the selected articles report users gaining actionable insights-but we do note a number of methodological issues that make these results difficult to interpret. We conclude that more work is needed to investigate the self-improvement hypothesis and provide a set of recommendations for future work.

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Section: Self-monitoring and Feedback Interventions Versus Pimentioning

confidence: 99%

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Dijk

Westerink

Beute

et al. 2017

Human–Computer Interaction

117

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“…[58][59][60] Attempts to improve adherence to ULT in individuals with gout have also been generally unsuccessful or marginally effective and difficult to sustain. There are particular additional impediments to improved adherence in patients with gout.…”

Section: Ethical Approvalmentioning

confidence: 99%

Effectiveness of an electronic patient-centred self-management tool for gout sufferers: a cluster randomised controlled trial protocol

et al. 2017

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IntroductionGout is increasing despite effective therapies to lower serum urate concentrations to 0.36 mmol/L or less, which, if sustained, significantly reduces acute attacks of gout. Adherence to urate-lowering therapy (ULT) is poor, with rates of less than 50% 1 year after initiation of ULT. Attempts to increase adherence in gout patients have been disappointing. We aim to evaluate the effectiveness of use of a personal, self-management, ‘smartphone’ application (app) to achieve target serum urate concentrations in people with gout. We hypothesise that personalised feedback of serum urate concentrations will improve adherence to ULT.Methods and analysisSetting and designPrimary care. A prospective, cluster randomised (by general practitioner (GP) practices), controlled trial.ParticipantsGP practices will be randomised to either intervention or control clusters with their patients allocated to the same cluster.InterventionThe intervention group will have access to the Healthy.me app tailored for the self-management of gout. The control group patients will have access to the same app modified to remove all functions except the Gout Attack Diary.Primary and secondary outcomesThe proportion of patients whose serum urate concentrations are less than or equal to 0.36 mmol/L after 6 months. Secondary outcomes will be proportions of patients achieving target urate concentrations at 12 months, ULT adherence rates, serum urate concentrations at 6 and 12 months, rates of attacks of gout, quality of life estimations and process and economic evaluations. The study is designed to detect a ≥30% improvement in the intervention group above the expected 50% achievement of target serum urate at 6 months in the control group: power 0.80, significance level 0.05, assumed ‘dropout’ rate 20%.Ethics and disseminationThis study has been approved by the University of New South Wales Human Research Ethics Committee. Study findings will be disseminated in international conferences and peer-reviewed journal.Trial registration numberACTRN12616000455460.

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“…The EPHPP tool has six sections, which are aggregated to a final grade on a three-point scale. Low study quality is a known issue in electronic interventions [75,76], but we did not exclude those studies. Rather, we checked if study quality moderates intervention efficacy, because a correspondence can be a sign of publication bias.…”

Section: Review Procedures and Moderatorsmentioning

confidence: 99%

Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression (Preprint)

Eckerstorfer¹,

Tanzer²,

Vogrincic-Haselbacher³

et al. 2018

Preprint

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Background: Mobile technology gives researchers unimagined opportunities to design new interventions to increase physical activity. Unfortunately, it is still unclear which elements are useful to initiate and maintain behavior change. Objective: In this meta-analysis, we investigated randomized controlled trials of physical activity interventions that were delivered via mobile phone. We analyzed which elements contributed to intervention success. Methods: After searching four databases and science networks for eligible studies, we entered k=49 studies with N=5764 participants into a random effects meta-analysis, controlling for baseline group differences. We also calculated a meta-regression with categories of intervention techniques for behavior change (education, goals, feedback/prompts, rewards, and social cues) as moderators. Results: We found a small overall effect of Hedges' g=.29, (95% CI: .20 to .37) which reduced to g=.22 after correcting for publication bias. None of the predefined elements (e.g., education) led to more intervention success by themselves, but a combination of goals and rewards showed that using goals and rewards simultaneously increased intervention efficacy by g=.31 compared to using none of the two elements. Conclusion: Overall, mHealth interventions to increase physical activity have a small to moderate effect. However, including functions related to goals and rewards led to greater intervention success. More research is needed to look at single behavior change techniques instead of categories. To achieve this, reporting interventions in trial registrations and articles should be more structured and more thorough.

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Using feedback through digital technology to disrupt and change habitual behavior: A critical review of current literature

Cited by 164 publications

References 98 publications

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Personal Informatics, Self-Insight, and Behavior Change: A Critical Review of Current Literature

Effectiveness of an electronic patient-centred self-management tool for gout sufferers: a cluster randomised controlled trial protocol

Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression (Preprint)

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