Adaptive Treatment Research Articles

A Sequential Adaptive Intervention Strategy Targeting Remission and Functional Recovery in Young People at Ultrahigh Risk of Psychosis

Clinical trials have not established the optimal type, sequence, and duration of interventions for people at ultrahigh risk of psychosis. To determine the effectiveness of a sequential and adaptive intervention strategy for individuals at ultrahigh risk of psychosis. The Staged Treatment in Early Psychosis (STEP) sequential multiple assignment randomized trial took place within the clinical program at Orygen, Melbourne, Australia. Individuals aged 12 to 25 years who were seeking treatment and met criteria for ultrahigh risk of psychosis according to the Comprehensive Assessment of At-Risk Mental States were recruited between April 2016 and January 2019. Of 1343 individuals considered, 342 were recruited. Step 1: 6 weeks of support and problem solving (SPS); step 2: 20 weeks of cognitive-behavioral case management (CBCM) vs SPS; and step 3: 26 weeks of CBCM with fluoxetine vs CBCM with placebo with an embedded fast-fail option of ω-3 fatty acids or low-dose antipsychotic medication. Individuals who did not remit progressed through these steps; those who remitted received SPS or monitoring for up to 12 months. Global Functioning: Social and Role scales (primary outcome), Brief Psychiatric Rating Scale, Scale for the Assessment of Negative Symptoms, Montgomery-Åsberg Depression Rating Scale, quality of life, transition to psychosis, and remission and relapse rates. The sample comprised 342 participants (198 female; mean [SD] age, 17.7 [3.1] years). Remission rates, reflecting sustained symptomatic and functional improvement, were 8.5%, 10.3%, and 11.4% at steps 1, 2, and 3, respectively. A total of 27.2% met remission criteria at any step. Relapse rates among those who remitted did not significantly differ between SPS and monitoring (step 1: 65.1% vs 58.3%; step 2: 37.7% vs 47.5%). There was no significant difference in functioning, symptoms, and transition rates between SPS and CBCM and between CBCM with fluoxetine and CBCM with placebo. Twelve-month transition rates to psychosis were 13.5% (entire sample), 3.3% (those who ever remitted), and 17.4% (those with no remission). In this sequential multiple assignment randomized trial, transition rates to psychosis were moderate, and remission rates were lower than expected, partly reflecting the ambitious criteria set and challenges with real-world treatment fidelity and adherence. While all groups showed mild to moderate functional and symptomatic improvement, this was typically short of remission. While further adaptive trials that address these challenges are needed, findings confirm substantial and sustained morbidity and reveal relatively poor responsiveness to existing treatments. ClinicalTrials.gov Identifier: NCT02751632.

Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China.

Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.

Predicting food craving in everyday life through smartphone-derived sensor and usage data.

Food craving relates to unhealthy eating behaviors such as overeating or binge eating and is thus a promising target for digital interventions. Yet, craving varies strongly across the day and is more likely in some contexts (external, internal) than in others. Prediction of food cravings ahead of time would enable preventive interventions. The objective of this study was to investigate whether upcoming food cravings could be detected and predicted from passive smartphone sensor data (excluding geolocation information) without the need for repeated questionnaires. Momentary food craving ratings, given six times a day for 14 days by 56 participants, served as the dependent variable. Predictor variables were environmental noise, light, device movement, screen activity, notifications, and time of the day recorded from 150 to 30 min prior to these ratings. Individual high vs. low craving ratings could be predicted on the test set with a mean area under the curve (AUC) of 0.78. This outperformed a baseline model trained on past craving values in 85% of participants by 14%. Yet, this AUC value is likely the upper bound and needs to be independently validated with longer data sets that allow a split into training, validation, and test sets. Craving states can be forecast from external and internal circumstances as these can be measured through smartphone sensors or usage patterns in most participants. This would allow for just-in-time adaptive interventions based on passive data collection and hence with minimal participant burden.

Comparing the effect of a multisectoral agricultural intervention on HIV-related health outcomes between widowed and married women

IntroductionWidowed women make up 18–40% of the 12 million women living with HIV in eastern and southern Africa. Widowhood has also been associated with greater HIV morbidity and mortality. We compared the effectiveness of a multisectoral climate adaptive agricultural livelihood intervention (called Shamba Maisha) on food insecurity, and HIV related health outcomes among widowed and married women living with HIV in western Kenya. MethodsWe implemented Shamba Maisha (NCT02815579) using a cluster-randomized control trial design. The intervention arm received an US$175 in-kind loan to purchase a micro-irrigation pump, seeds, and fertilizer, and received eight training sessions on sustainable agriculture and financial management. Study outcomes were measured every 6 months over a 24-month follow-up period and trends in outcomes assessed using multilevel mixed-effects models. ResultsThe trial enrolled 232 (61.5%) married and 145 (38.5%) widowed women. Widowed women (mean age 42.8 ± 8.4 years) were older than married women (35.8 ± 9.0 years) (p < 0.01). Almost all widowed women (97.2%) self-identified as household heads compared to 10.8% of married women. Comparing widowed vs married women, reduction in food insecurity (−3.13, 95%CI -4.42, −1.84 vs. −3.08, 95%CI -4.15, −2.02), depressive symptoms (−0.21, 95%CI -0.36, −0.07 vs. −0.19, 95%CI -0.29, −0.08), internalized stigma (−0.33, 95%CI -0.55, −0.11 vs. −0.38, 95%CI -0.57, −0.19), and anticipated stigma (−0.46 95%CI -0.65, −0.28 vs. −0.35, 95%CI -0.50, −0.21) was similar for both groups. In contrast, improvements in social support (−2.22, 95%CI -3.85, −0.59 vs. −4.00, 95%CI -5.16, −2.84; p = 0.08) and reduction in enacted stigma (0.01, 95%CI -0.06, 0.08 vs. −0.14, 95%CI -0.20, −0.09; p < 0.01) were weaker for widowed than married women. ConclusionsOur study is among the first comparing the effect of a livelihood intervention on HIV health outcomes among widowed and married women. Widowed women experienced similar benefits as married women on individual-level outcomes, but weaker benefit on outcomes dependent on their external environment like enacted stigma and social support. Future trials and programs targeting widowed women should bolster stigma reduction and social support.

Threats from weather events, urbanization and resilience: A case study of a coastal geography in India

ABSTRACT Urbanized coastal geographies with high population density but with low adaptive capacity are more exposed to threats of both rapid and slow onset weather events. Altered coasts along the Bay of Bengal is one such geography where general urbanization trend coupled with new policy driven tourism activity promotion is increasing exposure to frequent and intense disaster events. Current study takes a deeper dive into a 17 km long coastal stretch along Digha-Shankarpur-Mandarmoni in the Bay of Bengal in India. Community consultation using questionnaire-based survey is the primary source of evidence for this study. Community’s perception about threats from changing weather events and risks perceived by the coastal community engaged in various livelihood categories have been used to assess their resilience status, awareness of various local adaptive interventions and measures undertaken for local resilience building. With already predominant traditional agricultural and fisheries practices, promotion of tourism is altering the natural ecosystem faster through hard infrastructure expansion. The prevalent threats from weather events in the region lead to the loss of ecosystem services with adverse impacts on the communities whose livelihood are closely linked to the ecosystem service flows. In this context, how to build the resilience is the major research question. Nine impact indicators are used to assess risk using qualitative and quantitative methods. The analysis shows that individual and the community scale resilience increases with livelihood diversification, inclusive community engagement, polycentric governance structure, maintenance of diversity in ecosystem service flows, access to science based information on threats and opportunities, inclusion of local knowledge available with the communities in various livelihood categories for designing appropriate social protection measures. The study comes up with policy recommendations. It is important to avoid lack of long-term vision in the policies, enhance local institutional capacity through sector specific associations to deal with multiple threats quickly, overcome lack of awareness about preventive and soft adaptation measures, restoration actions, increase interaction and involvement of local stakeholders for local knowledge. Additionally, it is an urgent need to involve multiple even if sometimes conflicting scientific views on solutions vis-à-vis community wisdom for strengthening resilience.

36-LB: Dulce Digital-Me—A Randomized Comparative Effectiveness Trial of Static vs. Adaptive Digital Interventions for Hispanic Adults with Diabetes

We compared the effectiveness of Dulce Digital (DD), a diabetes education text messaging program, versus Dulce Digital-Me (DD-Me), which included DD text messaging plus algorithm-driven, personalized goal-setting and feedback, in improving diabetes outcomes in Hispanic adults with type 2 diabetes (T2D). In this open-label comparative effectiveness trial, Hispanic adults with T2D were randomized (1:1:1) to receive DD, or the more personalized DD-Me approach delivered via automated, algorithm-driven text messaging (DD-Me_Auto) or telephonic communication from a health coach (DD-Me_Tel) across 6-months as part of existing primary care. Changes in clinical (HbA1c—primary) and patient reported outcomes were examined over 12 months. We enrolled N=310 Hispanic participants (69.7% female; M=52.1±10.2 years; 93.2% Spanish preferred; HbA1c M= 9.3±1.6 %) to receive DD (n=107), DD-Me_Auto (n=106) or DD-Me_Tel (n=97). Mixed models revealed no difference between DD and DD-Me (combined Auto+Tel) in rate of HbA1c change across 6- or 12-months, ps&amp;gt;0.05. All groups showed clinically and statistically significant improvements in HbA1c across 6-months [DD ∆ = -0.17% per month post baseline (95% CI -0.22,-0.10); DD-Me_Auto ∆= -0.20% (95% CI -0.26,-0.14); DD-Me_Tel ∆ = -0.15% (95% CI -0.20,-0.10), ps&amp;lt;.001]. Effect sizes were slightly attenuated, but remained statistically significant at 12-months (∆ HbA1c= -0.08% to -0.05% per month; ps &amp;lt; 0.001). Self-management behaviors and diabetes distress improved across 12-months in all groups. DD-Me (combined) showed greater improvement in medication adherence versus DD over time (Bint=-0.20, 95% CI -0.33,-0.01, p&amp;lt; 0.01). Two low-cost digital interventions for Hispanic adults with low income and T2D had similar and clinically significant effects on HbA1c across 12 months. Simple digital approaches can be integrated within primary care-based chronic care models to reduce diabetes disparities. Disclosure A. Philis-tsimikas: Advisory Panel; Dexcom, Inc., Novo Nordisk A/S, Sanofi, Other Relationship; Medtronic, Research Support; Novo Nordisk A/S, Lilly, Viking Therapeutics, NIH - National Institutes of Health. K. L. Savin: None. J. A. Jones: None. M. Chichmarenko: None. H. Sandoval: None. L. Gallo: None. A. L. Fortmann: Employee; Dexcom, Inc. T. Clark: None. S. R. Spierling bagsic: None. E. Farcas: None. S. Roesch: None. J. H. Schultz: None. T. P. Gilmer: None. J. G. Godino: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK1123221UL1, TR002550)

STATUS OF CLIMATE VULNERABILLIES, HUMAN RESPONCIBILLIES AND CHALLENGES

As global climate change continues to manifest in various forms, the urgency to comprehensively assess its implications for human societies is paramount. This article presents a thorough examination of climate vulnerabilities and their imminent consequences, highlighting the critical role of human responsibility in addressing these challenges. The study synthesizes a wide range of empirical evidence and expert insights to provide an integrated assessment of the multifaceted impacts of climate change on various sectors, including ecosystems, health, agriculture, migration, and socio-economic equity. Drawing from a substantial body of literature, including reports from the Intergovernmental Panel on Climate Change (IPCC) and other reputable sources, this article underscores the complexity and interconnectedness of climate vulnerabilities. It delves into the consequences of rising temperatures, shifting precipitation patterns, and extreme weather events on ecosystems and biodiversity, underscoring the pressing need for conservation efforts. It’s elucidates the pronounced impacts of climate change on human health, infectious diseases, and food security, emphasizing the importance of adaptive strategies and policy interventions. The article also examines the intricate relationship between climate change and human migration, exploring how environmental stressors amplify societal vulnerabilities and raise ethical considerations. In sum, this article provides a comprehensive assessment of climate vulnerabilities and emphasizes the shared responsibility of human society in tackling these challenges. By recognizing the intricate web of consequences that climate change presents, this study serves as a call to action for policymakers, researchers, and individuals alike, urging coordinated efforts to safeguard our planet’s future.

665-P: Longitudinal Impact of Daily Experienced Diabetes Distress and Affective Symptoms on the Incidence and Remission of Depressive Symptoms—Results of the DIA-LINK 1 and 2 Studies

We analyzed whether daily experience of diabetes distress and affective symptoms, assessed with ecological momentary assessment (EMA) can predict incidence or remission of elevated depressive symptoms in people with type 1 and type 2 diabetes. In the DIA-LINK studies, 203 people with type 1 and 193 people with type 2 diabetes participated. In the 17-day EMA phase, participants completed daily prompts assessing different affective symptoms and diabetes distress. 358 participants completed the CES-Depression questionnaire at baseline and 3 months later. Incidence and remission of elevated depressive symptoms were defined by a cut-off score of 22. Area under receiver operating characteristics curves were used to analyse the predictive performance of EMA variables on incidence and remission of depressive symptoms. Incidence of elevated depressive symptoms was 18.3% while 32.3% showed a remission. Predictive performances of 15 different EMA variables are shown in Table 1. Daily experience of diabetes distress and affective symptoms can predict incidence and remission of elevated depressive symptoms. These variables could be used for designing just-in-time adaptive interventions for preventing elevated depressive symptoms in people with diabetes experiencing daily emotional burden of living with diabetes. Disclosure D.Ehrmann: Advisory Panel; Roche Diabetes Care, mySugr, Dexcom, Inc., Speaker's Bureau; Berlin-Chemie AG, Dexcom, Inc. A.J.Schmitt: None. B.Olesen: Advisory Panel; AstraZeneca, Evivamed, Berlin-Chemie AG, Speaker's Bureau; Medtronic, Medtrixx. B.Kulzer: Advisory Panel; Berlin-Chemie AG, Abbott Diabetes, Bayer Inc., Dexcom, Inc., Novo Nordisk, Insulet Corporation, embecta, Research Support; Sanofi, Roche Diabetes Care. N.Hermanns: Advisory Panel; Insulet Corporation, Research Support; Sanofi-Aventis Deutschland GmbH, Roche Diagnostics, Becton, Dickinson and Company, Speaker's Bureau; Dexcom, Inc., Berlin-Chemie AG. Funding German Center for Diabetes Research (FKZ82DZD1102A)

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers.

Intelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct. The metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group. The outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect. This study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions.

Stakeholder engagement in coastal adaptation interventions: Insights from the Southwest coast of Nigeria

Coastal areas in developing nations are in a highly precarious situation, making adaptation an urgent policy option for the region. However, since the ultimate success of many implemented adaptation decisions may only be known once the evolving impacts of climate change test them, greater attention may be devoted to the decision-making processes that shape adaptation choices. This study, therefore, focused on the stakeholder engagement process, which as an integral component of the adaptation process, unifies the efforts, commitments, and knowledge of different individuals and groups toward the implementation and success of coastal adaptation projects. The study applied an integrated approach comprising Stakeholder Analysis and Social Network Analysis tools to identify, categorize, and map stakeholders' relationships on the Southwest coast of Nigeria. The result highlights the multiplicity of stakeholders across administrative levels. Furthermore, it shows the disconsolate interest and detachment of statutory stakeholders notwithstanding their powers while portraying the high degree of local relations (autonomous communities and trade associations). The result further highlights the widespread adoption of public consultation as the approach of choice for stakeholder engagement, notwithstanding its flaws. The study's implications are evident in the prevalence of autonomously undertaken adaptation strategies that are overly narrowly focused, disjointed, and counterproductive. Addressing this issue must start with formulating a coastal zone management policy and establishing a coastal zone management agency as the coordinating body for all coastal matters across the study area.

Locally led adaptation: Promise, pitfalls, and possibilities

Locally led adaptation (LLA) has recently gained importance against top-down planning practices that often exclude the lived realities and priorities of local communities and create injustices at the local level. The promise of LLA is that adaptation would be defined, prioritised, designed, monitored, and evaluated by local communities themselves, enabling a shift in power to local stakeholders, resulting in more effective adaptation interventions. Critical reflections on the intersections of power and justice in LLA are, however, lacking. This article offers a nuanced understanding of the power and justice considerations required to make LLA useful for local communities and institutions, and to resolve the tensions between LLA and other development priorities. It also contributes to a further refinement of LLA methodologies and practices to better realise its promises. Ultimately, we argue that the utility of the LLA framing in promoting climate justice and empowering local actors needs to be tested empirically.

Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models

Adaptive mobile device-based health interventions often use machine learning models trained on non-mobile device data, such as social media text, due to the difficulty and high expense of collecting large text message (SMS) data. Therefore, understanding the differences and generalization of models between these platforms is crucial for proper deployment. We examined the psycho-linguistic differences between Facebook and text messages, and their impact on out-of-domain model performance, using a sample of 120 users who shared both. We found that users use Facebook for sharing experiences (e.g., leisure) and SMS for task-oriented and conversational purposes (e.g., plan confirmations), reflecting the differences in the affordances. To examine the downstream effects of these differences, we used pre-trained Facebook-based language models to estimate age, gender, depression, life satisfaction, and stress on both Facebook and SMS. We found no significant differences in correlations between the estimates and self-reports across 6 of 8 models. These results suggest using pre-trained Facebook language models to achieve better accuracy with just-in-time interventions.

Use of Behavior Function and Teacher Practices to Intensify Tier II Intervention: A Comparison of Check-In, Check-Out Adaptations

Within the context of a multi-tiered framework for behavior support, intervention intensification is warranted when a Tier II intervention fails to adequately address a targeted behavior. A traditional approach to intensification is to shift from standardized supports at Tier II to individualized and more resource-intensive supports at Tier III. Recent conceptualizations, however, reflect the use of a process-oriented, adaptive approach in which a continuum of responsive intensifications connects the framework’s tiers. Adaptive intervention design relies on the identification of critical factors to inform responsive adaptations to a standard treatment protocol. This study examined the distinct and relative influence of adaptations to Check In Check Out (CICO), a targeted intervention, informed by two critical factors—behavioral function and teachers’ use of behavior-specific praise. Results indicated that, relative to traditional CICO, CICO adapted to address behavioral function was less effective in producing differentiated levels of disruptive behavior. However, CICO adapted to strengthen a teaching practice—the delivery of behavior of behavior-specific praise—was effective in producing differentiated levels of disruptive behavior relative to the standard treatment protocol. Additional research on treatment adaptations informed by behavioral function and evidence-based teacher practices is needed to evaluate whether the variables may serve as critical factors within an adaptive approach to building treatment intensity at Tier II.

Abstract 51: Impacts of the COVID-19 Pandemic on Pediatric and Adolescent Cancer Care in Northern Tanzania

Abstract Purpose: Low- and middle-income countries (LMICs) have an increasing burden of pediatric cancer cases over recent years. In 2020, the COVID-19 pandemic further strained healthcare systems, presenting unique challenges for children requiring cancer care. The aim of this study was to assess differences in diagnoses, treatments, and outcomes of pediatric cancer patients before and during the COVID pandemic in Northern Tanzania. Methods: In this cross-sectional study, we assessed the demographic and clinical characteristics of pediatric and adolescent cancer patients (ages 0-18 years old) between 2016 and 2021 using the population-based Kilimanjaro Cancer Registry (KMCR). We categorized data into pre-COVID (2016-2019) and COVID (2020-2021) eras to perform descriptive analysis of the diagnosis, treatment, and demographic information before and during the COVID pandemic. We conducted inferential statistical analyses to compare all demographic and clinical variables, and used chi-square statistics for categorical variables with significance set at p&amp;lt;0.05. A secondary analysis was conducted on a subset of 101 children for whom cancer stage at diagnosis was recorded. This study received approval by the Kilimanjaro Christian Medical Centre (KCMC) and Duke University Institutional Review Boards and the National Institute for Medical Research. Results: Among the 417 patients included in this study, a higher proportion of patients during the COVID pandemic were diagnosed with hematological malignancies (48.2%) compared to patients diagnosed during the pre-COVID era (42.9%). Rates of chemotherapy (71.7%) and radiotherapy (6.8%) were lower during the pandemic compared to pre-COVID cases (83.8% and 9.6%, respectively), while rates of surgical treatments increased from 14.6% to 19.6% after 2019. However, only rates of chemotherapy were significantly different between pre-COVID and COVID era cases. Conclusion: Pediatric cancer care has changed in Tanzania during the COVID pandemic, with higher rates of hematologic malignancies and surgical therapies, but lower rates of chemotherapy and radiation therapy. Understanding the impact of the COVID pandemic on pediatric cancer care can help us better adapt healthcare systems and interventions to the emerging needs of children experiencing the compounding effects of cancer treatment and the global COVID crisis. Citation Format: Hannah Rice, Esther Majaliwa, Cesia Cotache-Condor, Yotham Gwanika, Nelson Chao, Kristin Schroeder, Henry Rice, Catherine Staton, Blandina Mmbaga, Emily Smith. Impacts of the COVID-19 Pandemic on Pediatric and Adolescent Cancer Care in Northern Tanzania [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 51.

The effects of adapted physical education sessions on the empathy of female students with overweight.

The global prevalence of childhood and adolescent overweight and obesity increases rapidly. Physical activity plays a major role in the prevention of obesity. The present study aimed to analyze the effect of adapted basketball sessions according to the empathic capacity of adolescent girls with overweight. Forty-two girls with overweight (age: 16.09 ± 0.85; years; height: 1.64 ± 0.67 m: weight: 73.02 ± 0.61 kg; BMI: 27.15 ± 1.37) volunteered to participate in the study and were randomly assigned to the experimental group (EG, n = 21) and control group (CG, n = 21). EG was submitted to a basketball intervention adapted to students with obesity while the CG performed classic basketball exercises for 7 weeks. Each week girls had 2 basketball teaching-learning sessions, lasting 50 min. The participants' empathy was assessed before and after the intervention using the Favre CEC. The results showed that adaptation intervention was associated with a significant emotional contagion decrease (Δ% = 0.466) and splitting with emotions (Δ% = 0.375), and with an empathy increase (Δ% = 1.387), in EG compared to CG. No significant difference was assessed in the empathy CG, before and after the intervention. This study demonstrated that adapted physical education classes could be an effective strategy to improve empathetic skills and inclusion of overweight girls as well as a means to prevent obesity.

Therapist training in video chat technology for use in an adaptive digital mental health intervention: Challenges, facilitators and implications for training models

AbstractObjectiveThis qualitative study investigated therapist training experiences, elements of skill acquisition, and barriers and facilitators associated with conducting assessments, and the delivery of low‐ and high‐intensity therapist assistance delivered via video chat technology, adjunctive to a transdiagnostic digital mental health intervention programme for anxiety and depression.MethodologyIn total, 34 semistructured interviews were conducted with 20 therapists. Twenty interviews explored experiences of training to administer a clinical assessment tool, and 14 additional interviews explored training experiences of delivering low‐ and high‐intensity therapist assistance via video chat technology.ResultsReflexive thematic analysis identified three themes: video chat skill acquisition, competencies transferrable to video chat and video chat service quality. Training and supervision were identified as important to scaffold skill development, and therapists described surprise that their skill set was transferrable to video chat. The most cited barrier to the adoption of video chat was the management of risk and distress, along with environmental suitability. Frequently cited facilitators to the adoption of video chat included stable Internet connection, protocols and resources.ConclusionThe benefits of video chat technology and digital mental health interventions can be maximised through the expansion and integration of training into existing teaching curricula. If therapists are familiarised and competent to deliver mental health services via the Internet, as well as in person, the future adoption of blended and stepped‐care models is likely to be increased.

Zoom-Based Mindfulness-Oriented Recovery Enhancement Plus Just-in-Time Mindfulness Practice Triggered by Wearable Sensors for Opioid Craving and Chronic Pain.

The opioid crisis in the USA remains severe during the COVID-19 pandemic, which has reduced access to evidence-based interventions. This Stage 1 randomized controlled trial (RCT) assessed the preliminary efficacy of Zoom-based Mindfulness-Oriented Recovery Enhancement (MORE) plus Just-in-Time Adaptive Intervention (JITAI) prompts to practice mindfulness triggered by wearable sensors (MORE + JITAI). Opioid-treated chronic pain patients (n = 63) were randomized to MORE + JITAI or a Zoom-based supportive group (SG) psychotherapy control. Participants completed ecological momentary assessments (EMA) of craving and pain (co-primary outcomes), as well as positive affect, and stress at one random probe per day for 90days. EMA probes were also triggered when a wearable sensor detected the presence of physiological stress, as indicated by changes in heart rate variability (HRV), at which time participants in MORE + JITAI were prompted by an app to engage in audio-guided mindfulness practice. EMA showed significantly greater reductions in craving, pain, and stress, and increased positive affect over time for participants in MORE + JITAI than for participants in SG. JITAI-initiated mindfulness practice was associated with significant improvements in these variables, as well as increases in HRV. Machine learning predicted JITAI-initiated mindfulness practice effectiveness with reasonable sensitivity and specificity. In this pilot trial, MORE + JITAI demonstrated preliminary efficacy for reducing opioid craving and pain, two factors implicated in opioid misuse. MORE + JITAI is a promising intervention that warrants investigation in a fully powered RCT. This study is registered on ClinicalTrials.gov (NCT04567043).

Incremental adaptation when transformation fails: The importance of place-based values and trust in governance in avoiding maladaptation

Climate change threatens human wellbeing and adaptation is essential. To-date, little research has examined connections between incremental and transformative adaptation. We address this gap using two multi-functional flood defence projects in Clontarf, a community in Dublin, Ireland, one of which represents transformative and the other incremental adaptation. Using a repeated study, we ask (i) does the importance of place-related values differ depending on whether adaptation is incremental or transformative, and (ii) what role does trust in governance play in incremental adaptation when transformation fails? Surveys were administered in Clontarf in 2014 (n = 280) after community resistance to transformative flood defences. A follow-up study using an identical survey was undertaken to evaluate separate incremental flood defences in 2016 (n = 242). Results highlight several important findings. First, both adaptation interventions show repeated potential threats to place from perceived weak governance rather than from disruptive place change caused by climate change. Second, where place attachment is strong, communities may repeatedly resist potential threats to place by challenging poor governance. However, this inadvertently threatens place disruption from climate change e.g., extreme climatic events. This could cause maladaptation, tying future decisions to past actions and failing to consider alternative transformative adaptation pathways. Finally, community discussions on transformative pathways and avoiding maladaptation risks are crucial for successful adaptation. This includes recognising trade-offs between place disruption threats from proposed adaptation strategies and climate change. Governance processes may subsequently need to transform and incorporate learnings or risk repeated resistance to adaptation previously considered rational. Many of these issues are likely to be encountered in all regions globally and across multiple adaptation sectors. Findings therefore provide important evidence to improve adaptation outcomes more generally.

Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures.

In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time. We propose modified Q-learning with generalized estimating equations to address these limitations and apply it to the M-bridge trial, which evaluates adaptive interventions to prevent problematic drinking among college freshmen. Simulation studies demonstrate our proposed method improves efficiency and robustness.

Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health.

Digital therapeutics (DTx, software as a medical device) provide personalized treatments for chronic diseases and expand precision medicine beyond pharmacogenomics-based pharmacotherapies. In this perspective article, we describe how DTx for chronic low back pain (CLBP) can be integrated with pharmaceutical drugs (e.g., NSAIDs, opioids), physical therapy (PT), cognitive behavioral therapy (CBT), and patient empowerment. An example of an FDA-authorized DTx for CLBP is RelieVRx, a prescription virtual reality (VR) app that reduces pain severity as an adjunct treatment for moderate to severe low back pain. RelieVRx is an immersive VR system that delivers at-home pain management modalities, including relaxation, self-awareness, pain distraction, guided breathing, and patient education. The mechanism of action of DTx is aligned with recommendations from the American College of Physicians to use non-pharmacological modalities as the first-line therapy for CLBP. Herein, we discuss how DTx can provide multimodal therapy options integrating conventional treatments with exposome-responsive, just-in-time adaptive interventions (JITAI). Given the flexibility of software-based therapies to accommodate diverse digital content, we also suggest that music-induced analgesia can increase the clinical effectiveness of digital interventions for chronic pain. DTx offers opportunities to simultaneously address the chronic pain crisis and opioid epidemic while supporting patients and healthcare providers to improve therapy outcomes.