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

Self-management interventions involving care partners of individuals with neurodegenerative movement disorders: a scoping review

PURPOSE

This scoping review aims to explore the components of existing self-management interventions involving care partners of individuals with neurodegenerative movement disorders (NMDs). The review seeks to describe the key components, categorize different types and approaches using the PRISMS taxonomy.

METHODS

A comprehensive search was conducted in five databases: SCOPUS, CINAHL, MEDLINE, PsycINFO, and Embase. Articles were included if they involved self-management interventions for adult care partners of individuals with NMDs. Data extraction was guided by the PRISMS, TEDSS and TIDieR frameworks, focusing on intervention characteristics, components, outcome measures, theories, and formats.

RESULTS

The review included 34 articles, with most interventions targeting Parkinson's Disease. Interventions varied widely in structure, delivery format, setting and content. Psychological and internal strategies were the most frequently utilized components. Notably, only 11 interventions were specifically designed for care partners.

CONCLUSION

This scoping review provides key insights into the diverse elements of self-management interventions for NMDs, including their structure, delivery format, and components. It highlights the lack of comprehensive dyadic interventions that support both individuals with NMDs and their care partners. Future research should explore the integration of personalization, communication training and dyadic coping strategies.

Evaluating clinical guidelines for chronic disease management: Do they enable the personalization of care?

OBJECTIVE

To described how general practitioners (GPs) personalize interventions for patients with chronic diseases and compare practice with the corresponding guidelines.

STUDY DESIGN

Scoping review followed by a multicentre cross-sectional study in French general practices.

METHODS

We identified elements of personalization described in guidelines related to diabetes, hypertension, dyslipidaemia, insomnia and depression. Then, GPs completed questionnaires for pharmacological (PI) and non-pharmacological interventions (NPI) after any consultation for these diseases to collect: when, on which the variables (clinical, biological characteristics, etc.), how and by whom the interventions were personalized, and what was personalized in the interventions. Agreement between GPs' practices and guidelines was analyzed using Cohen's Kappa.

RESULTS

We extracted 204 elements of personalization in 10 guidelines, and GPs described 1512 elements of personalization in 161 PI and 1313 elements in 131 NPI. Personalization was mainly based on patients' general characteristics (20.6 % of PT; 24.8 % of NPI) and treatments characteristics (14.5 % of PI; 9.8 % of NPI). GPs accounted for patients' preferences in 64.6 % of PI and 79.4 % of NPI. For PI, the agreement between GPs and guidelines was globally low (kappa = 0.21[0.11; 0.31]) but moderate for treatment characteristics (kappa = 0.48 [0.09; 0.87]) and high for disease characteristics (kappa = 1.00[1.00; 1.00]). For NPI, agreement was globally very low (kappa = 0.16[0.10; 0.25]) but moderate for treatment characteristics (kappa = 0.59[0.19; 1.00]) and disease characteristics (kappa = 0.48[0.12; 0.87]).

CONCLUSIONS

Guidelines insufficiently described the tailoring variables and the subsequent modifications of the interventions. They need to be better described to promote a medicine that is both personalized to each patient and homogeneous between physicians.

A co-design process to develop personalized mobility programming for individuals with mobility impairments

Introduction

Individuals with neurological conditions (e.g., stroke, spinal cord injury, multiple sclerosis) may experience challenges to their mobility. While the individual needs for persons with neurological conditions may vary, thus making intervention development more difficult, identifying key personalization or tailoring variables may help to customize interventions. However, the process to personalize treatments has not been well described. It is also unclear how adaptive intervention design includes the perspective of those with lived experience. Co-design methods may be a way to be transparent about intervention development to meet the needs of persons with mobility impairments while ensuring the resulting intervention is relevant and applicable to those who will be participating. The purpose of the present article is to describe a co-design process to facilitate the development of personalized mobility programming for persons with mobility impairments.

Methods

Development of a set of personalized mobility programming for individuals with mobility impairments was conducted following an adaptive intervention design approach with a co-design component. A series of working groups and individual sessions with key interest groups (e.g., persons with lived experience, fitness instructors, front-line clinicians, students) were conducted in order to develop the personalized mobility programming based on the needs and preferences described during various working groups.

Results

Two sets of working groups and three individual one-to-one sessions were conducted with a total of 14 participants (n = 6 persons with lived experience, n = 4 research team members, n = 2 physiotherapists, n = 2 occupational therapists, n = 1 registered kinesiologist). From the information gathered during the working groups a set of four personalized mobility programs were developed: (1) cognitive cardio class, (2) functional strength class, (3) mobility circuit group, and (4) an open gym. Participants also discussed the onboarding process, how to effectively track participant goals throughout the programming and personalization variables.

Discussion

The current paper provides a guideline for future work that aims to develop programming that is personalized to the needs of the persons with mobility impairments due to various neurological conditions. The strengths of this approach include the collaborative nature of the program development, while the main limitations were logistical in nature (e.g., scheduling, engaging all working group members).

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.