The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study.

BACKGROUND: The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms. OBJECTIVE: This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations. METHODS: In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG. RESULTS: The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36). CONCLUSIONS: This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

Defining suffering in pain. A systematic review on pain-related suffering using natural language processing.

ABSTRACT: Understanding, measuring, and mitigating pain-related suffering is a key challenge for both clinical care and pain research. However, there is no consensus on what exactly the concept of pain-related suffering includes, and it is often not precisely operationalized in empirical studies. Here, we (1) systematically review the conceptualization of pain-related suffering in the existing literature, (2) develop a definition and a conceptual framework, and (3) use machine learning to cross-validate the results. We identified 111 articles in a systematic search of Web of Science, PubMed, PsychINFO, and PhilPapers for peer-reviewed articles containing conceptual contributions about the experience of pain-related suffering. We developed a new procedure for extracting and synthesizing study information based on the cross-validation of qualitative analysis with an artificial intelligence-based approach grounded in large language models and topic modeling. We derived a definition from the literature that is representative of current theoretical views and describes pain-related suffering as a severely negative, complex, and dynamic experience in response to a perceived threat to an individual's integrity as a self and identity as a person. We also offer a conceptual framework of pain-related suffering distinguishing 8 dimensions: social, physical, personal, spiritual, existential, cultural, cognitive, and affective. Our data show that pain-related suffering is a multidimensional phenomenon that is closely related to but distinct from pain itself. The present analysis provides a roadmap for further theoretical and empirical development.

Specifying the Efficacy of Digital Therapeutic Tools for Depression and Anxiety: Retrospective, 2-Cohort, Real-World Analysis.

BACKGROUND: Depression and anxiety are the main sources of work and social disabilities as well as health-related problems around the world. Digital therapeutic solutions using cognitive behavioral therapy have demonstrated efficacy in depression and anxiety. A common goal of digital health apps is to increase user digital engagement to improve outcomes. However, there is a limited understanding of the association between digital platform components and clinical outcomes. OBJECTIVE: The aim of the study is to investigate the contribution of specific digital engagement tools to mental health conditions. We hypothesized that participation in coaching sessions and breathing exercises would be associated with a reduction in depression and anxiety. METHODS: Depression and general anxiety symptoms were evaluated in real-world data cohorts using the digital health platform for digital intervention and monitoring change. This retrospective real-world analysis of users on a mobile platform-based treatment followed two cohorts of people: (1) users who started with moderate levels of depression and completed at least 2 depression assessments (n=519) and (2) users who started with moderate levels of anxiety and completed at least 2 anxiety assessments (n=474). Levels of depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder-7) were tracked throughout the first 16 weeks. A piecewise mixed-effects model was applied to model the trajectories of the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 mean scores in 2 segments (1-6 weeks and 7-16 weeks). Finally, simple slope analysis was used for the interpretation of the interactions probing the moderators: coaching sessions and breathing exercises in both depression and anxiety cohorts. RESULTS: Analysis revealed a significant decrease in depression symptoms (ß=-.37, 95% CI -0.46 to 0.28; P≤.001) during the period of weeks 1-6 of app use, which was maintained during the period of 7-16 weeks. Coach interaction significantly moderated the reduction in depression symptoms during the period of weeks 1-6 (ß=-.03, 95% CI -0.05 to -0.001; P=.02). A significant decrease in anxiety symptoms (ß=-.41, 95% CI -0.50 to -0.33; P≤.001) was revealed during the period of 1-6 weeks, which was maintained during the period of 7-16 weeks. Breathing exercises significantly moderated the reduction in anxiety symptoms during the period of 1-6 weeks (ß=-.07, 95% CI -0.14 to -0.01; P=.04). CONCLUSIONS: This study demonstrated general improvement followed by a period of stability of depression and anxiety symptoms associated with cognitive behavioral therapy-based digital intervention. Interestingly, engagement with a coaching session but not a breathing exercise was associated with a reduction in depression symptoms. Moreover, breathing exercise but not engagement with a coaching session was associated with a reduction of anxiety symptoms. These findings emphasize the importance of using a personalized approach to behavioral health during digital health interventions.

Personalizing digital pain management with adapted machine learning approach.

Introduction: Digital therapeutics (DT) emerged and has been expanding rapidly for pain management. However, the efficacy of such approaches demonstrates substantial heterogeneity. Machine learning (ML) approaches provide a great opportunity for personalizing the efficacy of DT. However, the ML model accuracy is mainly associated with reduced clinical interpretability. Moreover, classical ML models are not adapted for the longitudinal nature of the DT follow-up data, which may also include nonlinear fluctuations. Objectives: This study presents an analytical framework for personalized pain management using piecewise mixed-effects model trees, considering the data dependencies, nonlinear trajectories, and boosting model interpretability. Methods: We demonstrated the implementation of the model with posture biofeedback training data of 3610 users collected during 8 weeks. The users reported their pain levels and posture quality. We developed personalized models for nonlinear time-related fluctuations of pain levels, posture quality, and weekly training duration using age, gender, and body mass index as potential moderating factors. Results: Pain levels and posture quality demonstrated strong improvement during the first 3 weeks of the training, followed by a sustained pattern. The age of the users moderated the time fluctuations in pain levels, whereas age and gender interactively moderated the trajectories in the posture quality. Train duration increased during the first 3 weeks only for older users, whereas all the users decreased the training duration during the next 5 weeks. Conclusions: This analytical framework offers an opportunity for investigating the personalized efficacy of digital therapeutics for pain management, taking into account users' characteristics and boosting interpretability and can benefit from including more users' characteristics.

The prognostic role of emotion regulation dynamics in the treatment of major depressive disorder.

OBJECTIVE: The potential prognostic role of emotion regulation in the treatment of major depressive disorder (MDD) has been highlighted by transtheoretical literature and supported by promising empirical findings. The majority of the literature is based on self-report observations at a single snapshot, thus little is known about the prognostic value of moment-to-moment dynamic evolvement of emotion. The present study is the first to examine the prognostic value of both intra- and interpersonal, moment-to-moment emotion regulation dynamics, and the potential moderating effect of the type of treatment. METHOD: To assess the prognostic value of emotion regulation dynamics, we focused on the first session, using 6,780 talk-turns within 52 patient-therapist dyads. Emotion regulation dynamics were measured using fundamental frequencies of the voice and were calculated using empirical Bayes residuals of the actor-partner interdependence model. Symptomatic change was measured using the Hamilton Rating Scale for Depression across 16 weeks of supportive treatment (ST) or supportive-expressive treatment (SET). RESULTS: Findings suggest that patients who show less regulated intrapersonal dynamics during the first session show less reduction of symptoms throughout treatment (ß = .26, p = .019). Findings further suggest that this association is mitigated when these patients receive SET, as opposed to ST (ß = .72, p = .020). CONCLUSIONS: The findings demonstrate the ability of first-session emotion regulation dynamics to serve as a prognostic variable. The findings further suggest that the adverse effect of emotion regulation dynamics on the patient's prognosis can be mitigated by explicit work on changing maladaptive emotional patterns. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Noninvasive Detection of Stress by Biochemical Profiles from the Skin.

Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group (n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.

Integrating exploration and prediction in computational psychotherapy science: proof of concept.

Introduction: Psychotherapy research has long preferred explanatory over predictive models. As a result, psychotherapy research is currently limited in the variability that can be accounted for in the process and outcome of treatment. The present study is a proof-of-concept approach to psychotherapy science that uses a datadriven approach to achieve robust predictions of the process and outcome of treatment. Methods: A trial including 65 therapeutic dyads was designed to enable an adequate level of variability in therapist characteristics, overcoming the common problem of restricted range. A mixed-model, data-driven approach with cross-validation machine learning algorithms was used to predict treatment outcome and alliance (within- and between-clients; client- and therapist-rated alliance). Results and discussion: Based on baseline predictors only, the models explained 52.8% of the variance for out-of-sample prediction in treatment outcome, and 24.1-52.8% in therapeutic alliance. The identified predictors were consistent with previous findings and point to directions for future investigation. Although limited by its sample size, this study serves as proof of the great potential of the presented approach to produce robust predictions regarding the process and outcome of treatment, offering a potential solution to problems such as p-hacking and lack of replicability. Findings should be replicated using larger samples and distinct populations and settings.

Multi-trajectory analysis uncovers latent associations between psychological and physiological acute stress response patterns.

Encounter with an acute stressor elicits multiple physiological and psychological response trajectories that spread at different times-scales and directions. Associating a single physiological response trajectory with a specific psychological response has remained a challenge, due to putative interactions between the different stress response pathways. Hence, multidimensional analysis of stress response trajectories may be better suited to account for response variability. To test this, 96 healthy female participants underwent a robust acute laboratory stress induction procedure while their psychological [positive and negative affect (PANAS)] and physiological [heart rate (HR), heart rate variability (HRV), saliva cortisol (CORT)] responses were recorded before, during and after stress. Combining these data using unsupervised group-based multi-trajectory modelling uncovered three latent classes that best accounted for variability across psychological and physiological stress response trajectories. These classes were labelled based on their psychological response patterns as: A prototypical response group that depict a moderate increase in negative and decrease in positive affect during stress, with both patterns recovering after stress offset (n = 55); A heightened response group that depict excessive affective responses during stress that recover after stress offset (n = 24); and a lack of recovery group that depict a moderate increase in negative and decrease in positive affect during stress, with both patterns not recovering after stress offset (n = 17). With respect to physiological acute stress trajectories, all three groups exhibited comparable increases in HR and CORT during stress that recovered after stress offset, yet only the prototypical group expressed the expected stress-induced reduction in HRV, while the other two groups exhibited blunted HRV response. Critically, focusing on a single physiological stress response trajectory, including HRV, did not account for psychological response variability and vice versa. Taken together, a multi-trajectory approach may better account for the multidimensionality of acute stress response and uncover latent associations between psychological and physiological response patterns. Compared to the other two groups, the prototypical group also exhibited significantly lower overall stress scores based on the DASS-21 scale. This, alongside the uncovered response patterns, suggest that latent psycho-physiological associations may shed light on stress response adaptivity or lack thereof.

Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors.

Background: Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources. Methods: For that, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data, alongside demographic features, were used to predict high versus low chronic stress with support vector machine classifiers, applying out-of-sample model testing. Results: The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate, heart-rate circadian characteristics), lifestyle (steps count, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification. Conclusion: As wearable technologies continue to rapidly evolve, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior.

Instructor-learner body coupling reflects instruction and learning.

It is widely accepted that nonverbal communication is crucial for learning, but the exact functions of interpersonal coordination between instructors and learners remain unclear. Specifically, it is unknown what role instructional approaches play in the coupling of physical motion between instructors and learners, and crucially, how such instruction-mediated Body-to-Body Coupling (BtBC) might affect learning. We used a video-based, computer-vision Motion Energy Analysis (MEA) to quantify BtBC between learners and instructors who used two different instructional approaches to teach psychological concepts. BtBC was significantly greater when the instructor employed a scaffolding approach than when an explanation approach was used. The importance of the instructional approach was further underscored by the fact that an increase in motion in the instructor was associated with boosted BtBC, but only during scaffolding; no such relationship between the instructor movements and BtBC was found during explanation interactions. Finally, leveraging machine learning approaches (i.e., support vector and logistic regression models), we demonstrated that both learning outcome and instructional approaches could be decoded based on BtBC. Collectively, these results show that the real-time interaction of teaching and learning bodies is important for learning and that the instructional approach matters, with possible implications for both in-person and online learning.

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study.

BACKGROUND: Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools. OBJECTIVE: By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month's BG levels. METHODS: In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association. RESULTS: Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=-2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=-3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=-6.42, P<.001) and diastolic (t=-4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP. CONCLUSIONS: The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

The two-stage therapeutic effect of posture biofeedback training on back pain and the associated mechanism: A retrospective cohort study.

Introduction: Back pain is an extremely common symptom experienced by people of all ages and the number one cause of disability worldwide.2 Poor posture has been identified as one of the factors leading to back pain. Digital biofeedback technology demonstrates the promising therapeutic ability in pain management through posture training. One common goal of such an approach is to increase users' posture awareness with associated movement correction. However, we lack a deep understanding of the biofeedback therapeutic mechanisms and the temporal dynamics of efficacy. Objective: This study investigates the temporal dynamics of the biofeedback learning process and associated outcomes in daily life settings, testing the mechanism of the biofeedback-associated pain reduction. Methods: This retrospective real-world evidence study followed 981 users who used the UpRight posture biofeedback platform. Piecewise mixed models were used for modeling the two-stage trajectory of pain levels, perceived posture quality, and weekly training duration following an 8-week biofeedback training. Also, the mediation effect of perceived posture quality on the analgesic effect of training duration was tested using Monte Carlo simulations based on lagged effect mixed models. Results: The analysis revealed significant pain level reduction (p <.0001) and posture quality improvement (p <.0001) during the first 4 weeks of the training, maintaining similar pain levels and perceived posture quality during the next 4 weeks. In addition, weekly training duration demonstrated an increase during the first 3 weeks (p <.001) and decreased during the next 5 weeks (p <.001). Moreover, training duration predicted following-week perceived posture quality (p <.001) and in turn perceived posture quality predicted following-week pain (p <.001) (p = 0.30). Finally, perceived posture quality mediated the effect of weekly training duration on the pain levels in 2 weeks (p <.0001). Conclusion: Our findings provide a better understanding of the therapeutic dynamic during digital biofeedback intervention targeting pain, modeling the associated two-stage process. Moreover, the study sheds light on the biofeedback mechanism and may assist in developing a better therapeutic approach targeting perceived posture quality.

Post-Induction High Adalimumab Drug Levels Predict Biological Remission at Week 24 in Patients With Crohn's Disease.

INTRODUCTION: We investigated whether early adalimumab drug levels (ADL) at week 4 predicted biological remission at week 24. METHODS: In a prospective study, we assessed clinical and biological remission at weeks 0, 4, 12, and 24 after induction of adalimumab in 33 patients with Crohn's disease. Disease activity was determined by the Harvey-Bradshaw Index, ileocolonoscopy reports, cross-sectional imaging, C-reactive protein (CRP), and fecal calprotectin (FC) levels. Clinical remission was defined as Harvey-Bradshaw Index <5. Biological remission was defined as a combination of FC < 200 µg/g and CRP <5 µg/mL. ADL trough levels were tested using a liquid phase, mobility shift assay. RESULTS: At 24 weeks, 18/33 (55%) of the patients were with biological remission. Ten (30%) patients required dose escalation or withdrawal from adalimumab by week 24 because of lack of response and exhibited significantly higher FC (P = 0.003) and CRP (P = 0.002). ADL levels at week 4 (19.8 µg/mL vs 10.2 µg/mL, P = 0.001) were significantly higher in patients with biological remission vs nonresponders at week 24. ADL levels at week 4 were a good predictor of biological remission at week 24, with area under the curve 0.86, 95% confidence interval (1.1; 1.67) and for combined biological and clinical remission, with area under the curve 0.8. The best ADL cutoff at week 4 that predicted biological remission at week 24 was 13.9 µg/mL (sensitivity 94.4% and specificity 73.3%). DISCUSSION: In individuals with Crohn's disease, higher adalimumab drug levels at week 4 (>13.9 µg/mL) were significantly associated with biological remission at week 24.

Abnormalities in emotional and motor reactions among young prehypertensive individuals: employing continuous blood pressure analysis.

BACKGROUND: Essential hypertension is an important risk factor for cerebrovascular diseases and a major cause of premature death in industrialized societies. A predisposing factor for essential hypertension is prehypertension: blood pressure (BP) values at rest that are at the higher end of the normal range. Abnormally enhanced cardiovascular responses to motor and emotional tasks have been found as predictors of essential hypertension. Yet, knowledge regarding the BP reaction to aversive stimuli and motor reaction in prehypertension is limited. METHODS: We compared the reaction to aversive and neutral stimuli inducing an emotional response (experiment 1) and to the isometric handgrip exercise (IHE) inducing a motor response (experiment 2), between prehypertensive and normotensive controls. BP reactions were measured and analyzed in a continuous fashion, in contrast to previous studies that averaged BP responses across blocks. We applied a multilevel B-spline model, a continuous analysis that enabled a better understanding of the BP time course and the detection of subtle differences between groups. RESULTS: In both tasks, we found that prehypertensive individuals showed enhanced DBP reactions compared with normotensive controls; prehypertensive individuals exhibited lower BP responses to aversive pictures and higher BP responses to the IHE. These results are in line with previous studies with healthy or hypertensive participants and suggest abnormalities already in the prehypertensive stage. CONCLUSION: Considering the high frequency and health risks related to prehypertension, understanding the autonomic reactions to emotional and motor stimuli in this population is of clinical and theoretical importance and could serve as a behavioural marker to identify at-risk groups.

Role of Digital Engagement in Diabetes Care Beyond Measurement: Retrospective Cohort Study.

BACKGROUND: The use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes. OBJECTIVE: This study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels. METHODS: This retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included "nontaggers" (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and "taggers" (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout. RESULTS: Analysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=-10.01, P<.001), which was maintained during the following 6 months (t=-1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=-11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20). CONCLUSIONS: This study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

Oxytocin synchrony between patients and therapists as a mechanism underlying effective psychotherapy for depression.

OBJECTIVE: Oxytocin (OT) synchrony has been suggested as a key mechanism by which bonds are formed and strengthened in various species, including those between mother and infant and between romantic partners. It is unknown whether such biological synchrony also plays a role in psychotherapy efficacy, where it may underlie the adverse effect of social impairment on the efficacy of treatment of depression. METHOD: Five hundred eighty OT saliva samples were collected from 37 patient-therapist dyads on a fixed schedule over a 16-session ongoing randomized controlled trial for psychotherapy for depression. Biological synchrony was operationalized as the correlation between changes occurring repeatedly over treatment in patient and therapist OT levels pre- to postsession. RESULTS: OT synchrony between patients and therapists was found to be associated with effective treatment. The findings support the proposed mediation model: (a) poorer social functioning at baseline predicted lower levels of patient-therapist synchrony in OT changes from pre- to postsession over the course of treatment; (b) lower levels of therapist-patient OT synchrony, in turn, predicted less reduction in depressive symptoms during treatment; and (c) based on quasi-Bayesian Monte Carlo simulations, the levels of therapist-patient synchrony significantly mediated the association between social impairment and reduction in depressive symptoms. Findings were replicated using robust inferential methods. CONCLUSIONS: The findings suggest that OT synchrony between patient and therapist may be a biological mechanism by which impaired interpersonal functioning undermines treatment outcome. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A randomized controlled trial for identifying the most suitable treatment for depression based on patients' attachment orientation.

OBJECTIVE: Many active treatments exist for major depressive disorder (MDD), but little is known about their differential effects for various subpopulations of patients to guide precision medicine. This is the first randomized controlled trial (RCT) designed to identify differential treatment effects based on patients' attachment orientations. We tested an a priori preregistered hypothesis of the potential moderating effect of patients' attachment orientation on the outcome of supportive therapy (ST) versus supportive-expressive therapy (SET). METHODS: The RCT was conducted between 2015 and 2021. Individuals with MDD were randomly assigned to 16-week ST or SET. The predefined primary outcome measure was the Hamilton Rating Scale for Depression. Hypotheses were formulated and preregistered before data collection. RESULTS: One hundred patients with MDD were enrolled, 57% women, average age 31.2 (SD = 8.25). Data were analyzed using the intention-to-treat approach. Our hypothesis that attachment anxiety is a significant moderator of treatment outcome was supported (B = -0.09, p = .016): Patients with higher levels of attachment anxiety showed greater treatment efficacy following SET than ST. Although the hypothesis regarding a potential moderating effect of avoidant attachment was not supported, sensitivity analyses revealed that individuals with disorganized attachment orientation (higher scores on both anxious and avoidant attachment) benefited more from SET than from ST (B = -0.07, p = .04). CONCLUSION: The findings support the clinical utility of patients' attachment orientation in selecting the most suitable treatment for individuals and demonstrate the methodological utility of RCTs predesigned to test theoretically based models of personalized treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

What do these sounds tell us about the therapeutic alliance: Acoustic markers as predictors of alliance.

Predicting the trajectories of alliance formation that the patient is likely to establish with the therapist during treatment, even before their first meeting, can help prevent the potentially harmful consequences of deterioration in alliance, such as poor outcome and premature dropout. The present study aimed to examine the ability of four pretreatment acoustic markers to predict the alliance that is likely to be formed in the course of treatment: F0 span, speech rate, pause proportion and jitter. Data from 560 observations of 38 patients were collected as part of an ongoing randomized clinical trial of short-term psychotherapy for major depressive disorder. The acoustic markers were measured using high-quality recordings at baseline, before the patient and therapist ever met or had any type of communication. A multilevel model was used to examine the ability of the four acoustic markers to predict the slopes of alliance formation in the course of treatment, all markers being introduced in the same model. The clinical utility of the acoustic markers was explored in two case studies. The model explained 22% of the variance in alliance formation. Higher levels of both jitter and pause proportion at baseline predicted less strengthening of the alliance in the course of treatment. The findings, which should be replicated in larger samples, suggest that much of the therapeutic alliance can be predicted based on the acoustic characteristics of the patient's voice in the first 3 min of their intake, before they even meet their therapist.