Adherence Reporting in Randomized Controlled Trials Examining Manualized Multisession Online Interventions: Systematic Review of Practices and Proposal for Reporting Standards

Beintner, Ina; Vollert, Bianka; Zarski, Anna-Carlotta; Bolinski, Felix; Musiat, Peter; Görlich, Dennis; Ebert, David Daniel; Jacobi, Corinna

doi:10.2196/14181

Cited by 72 publications

(63 citation statements)

References 33 publications

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“…Adherence in iCBT needs further exploration. In Beintner and colleagues’ review [ 21 ], most studies (85%) reported at least 1 adherence indicator, yet adherence metrics varied widely across this literature. The 10 most commonly reported adherence metrics were as follows: full intervention completion; completion of a minimum number of sessions/modules; average number of completed sessions/modules; specified point of discontinuation of the intervention (“dropout”); dropout without specifying a time point; number of participants who were randomized to an intervention group but never logged on; number of times a participant logged on to access the intervention program; total time spent on the program; number of entries into a diary; and number of messages sent to a coach.…”

Section: Discussionmentioning

confidence: 99%

“…The 10 most commonly reported adherence metrics were as follows: full intervention completion; completion of a minimum number of sessions/modules; average number of completed sessions/modules; specified point of discontinuation of the intervention (“dropout”); dropout without specifying a time point; number of participants who were randomized to an intervention group but never logged on; number of times a participant logged on to access the intervention program; total time spent on the program; number of entries into a diary; and number of messages sent to a coach. Recognizing that the specific metric(s) chosen will need to be appropriate to the specific components or delivery formats of online interventions, Beintner and colleagues [ 21 ] further recommend the use of multiple adherence metrics, which all studies should report on adherence, providing detailed information on its operationalization.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of online programming for mental health interventions, adherence has been defined as “the extent to which individuals experience or engage with content” [ 21 ]. However, operationalizing adherence to iCBT interventions poses challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Since there are few studies examining the long-term impact of self-guided iCBT interventions, the primary aim of this within-group analysis was to assess 6- and 12-month follow-up outcomes of trial participants who received immediate access to the Thrive intervention. Moreover, to date, there has been no consensus on the operationalization or impact of adherence on outcomes in iCBT interventions [ 21 ]. Thus, our secondary aim was to determine whether program adherence enhanced the effectiveness of the Thrive intervention.…”

Section: Introductionmentioning

confidence: 99%

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Twelve-Month Follow-Up to a Fully Automated Internet-Based Cognitive Behavior Therapy Intervention for Rural Adults With Depression Symptoms: Single-Arm Longitudinal Study

Schure¹,

McCrory²,

Franklin³

et al. 2020

J Med Internet Res

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Background Internet-based cognitive behavior therapy (iCBT) interventions have the potential to help individuals with depression, regardless of time and location. Yet, limited information exists on the longer-term (>6 months) effects of iCBT and adherence to these interventions. Objective The primary aim of this study was to evaluate the longitudinal (12 months) effectiveness of a fully automated, self-guided iCBT intervention called Thrive, designed to enhance engagement with a rural population of adults with depression symptoms. The secondary aim was to determine whether the program adherence enhanced the effectiveness of the Thrive intervention. Methods We analyzed data from 181 adults who used the Thrive intervention. Using self-reports, participants were evaluated at baseline, 8 weeks, 6 months, and 12 months for the primary outcome of depression symptom severity using the Patient Health Questionnaire-9 (PHQ-9) scale and secondary outcome measures, namely, the Generalized Anxiety Disorder Scale-7 (GAD-7) scores, Work and Social Adjustment Scale (WSAS) scores, Conner-Davidson Resilience Scale-10 (CD-RISC-10) scores, and suicidal ideation (ninth item of the PHQ-9 scale) scores. The Thrive program adherence was measured using the numbers of program logins, page views, and lessons completed. Results The assessment response rates for 8-week, 6-month, and 12-month outcomes were 58.6% (106/181), 50.3% (91/181), and 51.4% (93/181), respectively. By 8 weeks, significant improvements were observed for all outcome measures. These improvements were maintained at 12 months with mean reductions in severities of depression (mean –6.5; P<.001) and anxiety symptoms (mean –4.3; P<.001). Improvements were also observed in work and social functioning (mean –6.9; P<.001) and resilience (mean 4.3; P<.001). Marked decreases were observed in suicidal ideation (PHQ-9 ninth item score >1) at 6 months (16.5%) and 12 months (17.2%) compared to baseline (39.8%) (P<.001). In regard to the program adherence, cumulative counts of page views and lessons completed were significantly related to lower PHQ-9, GAD-7, and WSAS scores and higher CD-RISC-10 scores (all P values <.001 with an exception of page views with WSAS for which P value was .02). Conclusions The Thrive intervention was effective at reducing depression and anxiety symptom severity and improving functioning and resilience among a population of adults from mostly rural communities in the United States. These gains were maintained at 1 year. Program adherence, measured by the number of logins and lessons completed, indicates that users who engage more with the program benefit more from the intervention. Trial Registration ClinicalTrials.gov NCT03244878; https://clinicaltrials.gov/ct2/show/NCT03244878

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Twelve-Month Follow-Up to a Fully Automated Internet-Based Cognitive Behavior Therapy Intervention for Rural Adults With Depression Symptoms: Single-Arm Longitudinal Study

Schure¹,

McCrory²,

Franklin³

et al. 2020

J Med Internet Res

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“…In face-to-face randomized controlled trials (RCTs), patient engagement has been found to be an essential predictor of intervention outcomes, with higher levels of patient engagement predicting better health outcomes of participants [ 11 ]. However, such a relationship has not yet been confirmed in mHealth interventions [ 12 ]. Findings regarding patient engagement and intervention outcomes in mHealth interventions are mixed.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Patient Engagement and Depressive Symptoms Among People Living With HIV in a Mobile Health Intervention: Secondary Analysis of a Randomized Controlled Trial

Zeng¹,

Guo²,

Li³

et al. 2020

JMIR Mhealth Uhealth

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Background Associations between higher levels of patient engagement and better health outcomes have been found in face-to-face interventions; studies on such associations with mobile health (mHealth) interventions have been limited and the results are inconclusive. Objective The objective of this study is to investigate the relationship between patient engagement in an mHealth intervention and depressive symptoms using repeated measures of both patient engagement and patient outcomes at 4 time points. Methods Data were drawn from a randomized controlled trial (RCT) of an mHealth intervention aimed at reducing depressive symptoms among people living with HIV and elevated depressive symptoms. We examined the association between patient engagement and depressive symptoms in the intervention group (n=150) where participants received an adapted cognitive-behavioral stress management (CBSM) course and physical activity promotion on their WeChat social media app. Depressive symptoms were repeatedly measured using the Patient Health Questionnaire (PHQ-9) at baseline and 1 month, 2 months, and 3 months. Patient engagement was correspondingly measured by the completion rate, frequency of items completed, and time spent on the program at 1 month, 2 months, and 3 months. Latent growth curve models (LGCMs) were used to explore the relationship between patient engagement and depressive symptoms at multiple time points in the intervention. Results The mean PHQ-9 scores were 10.2 (SD 4.5), 7.7 (SD 4.8), 6.5 (SD 4.7), and 6.7 (SD 4.1) at baseline, 1 month, 2 months, and 3 months, respectively. The mean completion rates were 50.6% (SD 31.8%), 51.5% (SD 32.2%), and 50.8% (SD 33.7%) at 1, 2, and 3 months, respectively; the average frequencies of items completed were 18.0 (SD 14.6), 32.6 (SD 24.8), and 47.5 (SD 37.2) at 1, 2, and 3 months, respectively, and the mean times spent on the program were 32.7 (SD 66.7), 65.4 (SD 120.8), and 96.4 (SD 180.4) minutes at 1, 2, and 3 months, respectively. LGCMs showed good model fit and indicated that a higher completion rate (β at 3 months=–2.184, P=.048) and a greater frequency of items completed (β at 3 months=–0.018, P=.04) were associated with fewer depressive symptoms at 3 months. Although not significant, similar trends were found in the abovementioned relationships at 1 and 2 months. There was no significant relationship between time spent on the program and depressive symptoms. Conclusions This study revealed a positive association between patient engagement and health outcomes at 3 months of an mHealth intervention using LGCMs and repeated measures data. The results underscore the importance of improving patient engagement in mHealth interventions to improve patient-centered health outcomes. Trial Registration Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://tinyurl.com/yxb64mef International Registered Report Identifier (IRRID) RR2-10.1186/s12889-018-5693-1

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Predictors, mediators, and moderators of response to digital interventions for eating disorders: A systematic review

McClure,

Fuller‐Tyszkiewicz,

Messer

et al. 2023

Intl J Eating Disorders

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ObjectiveDigital interventions show promise as an effective prevention or self‐management option for eating disorders (EDs). However, it remains unclear how, for whom, and through what mechanisms they work in this population, as a synthesis of outcome predictors, moderators, and mediators is lacking. This systematic review synthesized empirical research investigating predictors, mediators, and moderators of response to digital interventions for EDs.MethodSix databases were searched (PROSPERO CRD42022295565) for studies that assessed predictors, moderators, or mediators of response (i.e., uptake, drop‐out, engagement, and symptom level change) to a digital prevention or treatment program for EDs. Variables were grouped into several overarching categories (demographic, symptom severity, psychological, etc.) and were synthesized qualitatively across samples without a formally diagnosed ED (typically prevention‐focused) and samples with a formally diagnosed ED (typically treatment‐focused).ResultsEighty‐six studies were included. For studies recruiting samples without a formal diagnosis (n = 70 studies), most predictors explored were statistically unrelated to outcome, although participant age, baseline symptom severity, confidence to change, motivation, and program engagement showed preliminary evidence of prognostic potential. No robust moderators or mediators were identified. Few studies recruiting samples with a formal diagnosis emerged (n = 16), of which no reliable predictors, moderators, or mediators were identified.DiscussionIt remains unclear how, for whom, and under what circumstances digital programs targeting EDs work. We offer several recommendations for future research with the aim of advancing understanding of client characteristics and intervention elements that signal success from this intervention modality.Public SignificanceDigital interventions have shown potential as an effective, scalable, and accessible intervention option for EDs. However, responsiveness varies, so advancing understanding of predictors, mediators, and moderators of outcome to digital interventions for EDs is needed. Such knowledge is important for enabling safe and efficient treatment matching, and for informing future development of effective digital interventions.

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Adherence Reporting in Randomized Controlled Trials Examining Manualized Multisession Online Interventions: Systematic Review of Practices and Proposal for Reporting Standards

Cited by 72 publications

References 33 publications

Twelve-Month Follow-Up to a Fully Automated Internet-Based Cognitive Behavior Therapy Intervention for Rural Adults With Depression Symptoms: Single-Arm Longitudinal Study

Twelve-Month Follow-Up to a Fully Automated Internet-Based Cognitive Behavior Therapy Intervention for Rural Adults With Depression Symptoms: Single-Arm Longitudinal Study

Relationship Between Patient Engagement and Depressive Symptoms Among People Living With HIV in a Mobile Health Intervention: Secondary Analysis of a Randomized Controlled Trial

Predictors, mediators, and moderators of response to digital interventions for eating disorders: A systematic review

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