Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis

Xu, Zhan; Smit, Eline Suzanne

doi:10.1177/20552076231183543

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…1 First, little research has summarized the efficacy of JITAIs for behavioral health conditions. Most commonly, systematic reviews and meta-analyses have examined the impact of JITAIs on the promotion of physical health, providing some early support for JITAIs as a digital health tool [32,33]. The extant systematic reviews relevant to behavioral health have evaluated JITAIs either within a larger health promotion framework or for a specific psychiatric disorder.…”

Section: Discussionmentioning

confidence: 99%

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The Efficacy of Just-In-Time Adaptive Interventions in Augmenting Behavioral Health: Protocol for a Systematic Review (Preprint)

Henry,

Blay-Tofey,

Haeffner

et al. 2024

Preprint

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BACKGROUND Just-in-time adaptive interventions (JITAIs) use mobile, digital tools to provide individuals with personalized interventions at the optimal time and in the optimal context. Accordingly, JITAIs are promising for advancing accessible, equitable, and evidence-based treatment for behavioral health. To guide future work in this space, research is needed to examine the efficacy of JITAIs for behavioral health conditions and better understand their mechanisms of action. OBJECTIVE In the proposed systematic review, we will investigate the efficacy of JITAIs for improving (1) distal outcomes (here, behavioral health) and (2) proximal outcomes (eg, emotion regulation). METHODS This systematic review is being conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses Protocol. We developed our search strategy and executed the literature search in collaboration with biomedical librarians; five databases (PubMed, Embase, Cochrane Library, Web of Science: Core Collection, and APA PsycINFO) were searched and results were managed using EndNote 20. We are screening all records in duplicate in Covidence according to eligibility criteria; title/abstract screening is complete and full-text screening is ongoing. Data items will be extracted, and risk of bias will be assessed in duplicate from the included articles in Covidence. RESULTS We will summarize JITAI characteristics in tables and text. We will conduct meta-analyses for the distal and proximal outcomes conditional upon sufficient homogeneity in subgroups. Moderation (conditional upon sufficient heterogeneity of outcomes) and mediation (ie, whether changes in proximal outcomes mediate the relation between JITAIs and distal outcomes) will be conducted, as appropriate. We will investigate publication bias and use the Grading of Recommendations Assessment, Development and Evaluation to characterize the quality of evidence of our estimates. CONCLUSIONS Here, we propose a systematic review to assess the state of the literature on JITAIs for behavioral health. The insights derived from this study will reinforce JITAI definitions, clarify JITAI components, describe the efficacy of JITAIs in augmenting distal and proximal behavioral health outcomes, and inform the next steps in JITAI research.

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Section: Discussionmentioning

confidence: 99%

“…Previous research has typically evaluated JITAIs as digital health tools for the promotion of physical health [32,33]. While a select few reviews have examined JITAIs for specific psychiatric disorders [13], research has yet to robustly summarize the impact of JITAIs for behavioral health conditions.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

The Efficacy of Just-In-Time Adaptive Interventions in Augmenting Behavioral Health: Protocol for a Systematic Review (Preprint)

Henry,

Blay-Tofey,

Haeffner

et al. 2024

Preprint

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Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses

Ehrmann,

Hermanns,

Schmitt

et al. 2024

Diabetologia

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Aims/hypothesis Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up. Methods In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up. Results Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; p<0.0001) and perceived hyperglycaemia (t=13.637; p<0.0001) were the strongest predictors of daily diabetes distress, while CGM-based glucose variability was not significantly associated (t=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (β=0.32), diabetes distress (β=0.39) and hypoglycaemia fear (β=0.34) at follow-up (all p<0.001). Individuals with a stronger association between CGM-measured glucose control and daily distress had higher levels of psychosocial well-being at follow-up (depressive symptoms: β=−0.31; diabetes distress: β=−0.33; hypoglycaemia fear: β=−0.27; all p<0.001) but also higher HbA1c (β=0.12; p<0.05). Conclusions/interpretation Overall, subjective perceptions of glucose seem to be more influential on diabetes distress than objective CGM parameters of glycaemic control. N-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach. Graphical Abstract

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Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial

Abusamaan,

Ballreich,

Dobs

et al. 2024

Trials

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Background Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches. Methods This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC’s benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18–75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m2 (≥ 23 kg/m2 for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA. Discussion Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings. Trial registration ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376

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Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis

Cited by 7 publications

References 66 publications

The Efficacy of Just-In-Time Adaptive Interventions in Augmenting Behavioral Health: Protocol for a Systematic Review (Preprint)

The Efficacy of Just-In-Time Adaptive Interventions in Augmenting Behavioral Health: Protocol for a Systematic Review (Preprint)

Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial

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