A classification of methods used to personalize participative interventions revealed inadequate reporting in trial protocols

A methodological framework allows the identification of personomic markers to consider when designing personalized interventions

OBJECTIVES

To develop a methodological framework to identify and prioritize personomic markers (e.g., psychosocial situation, beliefs…) to consider for personalizing interventions and to test in smoking cessation interventions.

STUDY DESIGN AND SETTING

(1) We identified potential personomic markers considered in protocols of personalized interventions, in reviews of predictors of smoking cessation, and in interviews with general practitioners. (2) Physicians, and patient smokers or former smokers selected the markers they considered most relevant during online paired comparison experiments. Data were analyzed with Bradley Terry Luce models.

RESULTS

Thirty-six personomic markers were identified from research evidence. They were evaluated by 795 physicians (median age: 34, IQR [30-38]; 95% general practitioners) and 793 patients (median age: 54, IQR [42-64], 71.4% former smokers) during 11,963 paired comparisons. Physicians identified patients' motivation for quitting (e.g., Prochaska stages), patients' preferences, and patients' fears and beliefs (e.g., concerns about weight gain) as the most relevant elements to personalize smoking cessation. Patients considered their motivation for quitting, smoking behavior (e.g., smoking at home/at work), and tobacco dependence (e.g., Fagerström Test) as the most relevant elements to consider.

CONCLUSION

We provide a methodological framework to prioritize which personomic markers should be considered when developing smoking cessation interventions.

A systematic scoping review of just-in-time, adaptive interventions finds limited automation and incomplete reporting

OBJECTIVES

To describe the degree of automation in just-in-time, adaptive interventions (JITAIs) assessed in randomized controlled trials (RCTs) in any medical specialty, and to assess the completeness of intervention reporting.

STUDY DESIGN AND SETTING

Systematic scoping review-we searched PubMed, PsycINFO, and Web of Science, from 1 January 2019 to 2 March 2021, for reports of RCTs assessing JITAIs. We assessed whether study reports included the minimum information required to replicate the interventions based on JITAI frameworks. We described JITAIs according to their automation level using an established framework (partially, highly, or fully automated), and care workload distribution (requiring work from patients, health care professionals [HCPs], both, or neither).

RESULTS

We included 88 JITAIs (62%, n = 55 supported chronic illness management and 12%, n = 11 supported health behavior change). Overall, 77% (n = 68) of JITAIs were missing some information required to replicate the intervention (e.g., n = 38, 43% inadequately reported the algorithm used to select intervention components). Only fifteen (17%) JITAIs were fully automated and did not require additional work from HCPs nor patients. Of the remaining JITAIs, 36% required work from both patients and HCPs, and 47% required work from either patients or HCPs.

CONCLUSION

Most JITAIs are not fully automated and require work from the HCPs and patients.

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Protocol Development for an Expert System to Provide Adaptive User Feedback

BACKGROUND

Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions.

OBJECTIVE

To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder.

METHODS

Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system.

RESULTS

Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers.

CONCLUSIONS

Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems.