Remote and unsupervised digital memory assessments can reliably detect cognitive impairment in Alzheimer's disease.

INTRODUCTION: Remote unsupervised cognitive assessments have the potential to complement and facilitate cognitive assessment in clinical and research settings. METHODS: Here, we evaluate the usability, validity, and reliability of unsupervised remote memory assessments via mobile devices in individuals without dementia from the Swedish BioFINDER-2 study and explore their prognostic utility regarding future cognitive decline. RESULTS: Usability was rated positively; remote memory assessments showed good construct validity with traditional neuropsychological assessments and were significantly associated with tau-positron emission tomography and downstream magnetic resonance imaging measures. Memory performance at baseline was associated with future cognitive decline and prediction of future cognitive decline was further improved by combining remote digital memory assessments with plasma p-tau217. Finally, retest reliability was moderate for a single assessment and good for an aggregate of two sessions. DISCUSSION: Our results demonstrate that unsupervised digital memory assessments might be used for diagnosis and prognosis in Alzheimer's disease, potentially in combination with plasma biomarkers. HIGHLIGHTS: Remote and unsupervised digital memory assessments are feasible in older adults and individuals in early stages of Alzheimer's disease. Digital memory assessments are associated with neuropsychological in-clinic assessments, tau-positron emission tomography and magnetic resonance imaging measures. Combination of digital memory assessments with plasma p-tau217 holds promise for prognosis of future cognitive decline. Future validation in further independent, larger, and more diverse cohorts is needed to inform clinical implementation.

A complex systems perspective on chronic aggression and self-injury: case study of a woman with mild intellectual disability and borderline personality disorder.

BACKGROUND: Challenging behaviors like aggression and self-injury are dangerous for clients and staff in residential care. These behaviors are not well understood and therefore often labeled as "complex". Yet it remains vague what this supposed complexity entails at the individual level. This case-study used a three-step mixed-methods analytical strategy, inspired by complex systems theory. First, we construed a holistic summary of relevant factors in her daily life. Second, we described her challenging behavioral trajectory by identifying stable phases. Third, instability and extraordinary events in her environment were evaluated as potential change-inducing mechanisms between different phases. CASE PRESENTATION: A woman, living at a residential facility, diagnosed with mild intellectual disability and borderline personality disorder, who shows a chronic pattern of aggressive and self-injurious incidents. She used ecological momentary assessments to self-rate challenging behaviors daily for 560 days. CONCLUSIONS: A qualitative summary of caretaker records revealed many internal and environmental factors relevant to her daily life. Her clinician narrowed these down to 11 staff hypothesized risk- and protective factors, such as reliving trauma, experiencing pain, receiving medical care or compliments. Coercive measures increased the chance of challenging behavior the day after and psychological therapy sessions decreased the chance of self-injury the day after. The majority of contemporaneous and lagged associations between these 11 factors and self-reported challenging behaviors were non-significant, indicating that challenging behaviors are not governed by mono-causal if-then relations, speaking to its complex nature. Despite this complexity there were patterns in the temporal ordering of incidents. Aggression and self-injury occurred on respectively 13% and 50% of the 560 days. On this timeline 11 distinct stable phases were identified that alternated between four unique states: high levels of aggression and self-injury, average aggression and self-injury, low aggression and self-injury, and low aggression with high self-injury. Eight out of ten transitions between phases were triggered by extraordinary events in her environment, or preceded by increased fluctuations in her self-ratings, or a combination of these two. Desirable patterns emerged more often and were less easily malleable, indicating that when she experiences bad times, keeping in mind that better times lie ahead is hopeful and realistic.

Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study.

BACKGROUND: Referred to as the "silent killer," elevated blood pressure (BP) often goes unnoticed due to the absence of apparent symptoms, resulting in cumulative harm over time. Chronic stress has been consistently linked to increased BP. Prior studies have found that elevated BP often arises due to a stressful lifestyle, although the effect of exact stressors varies drastically between individuals. The heterogeneous nature of both the stress and BP response to a multitude of lifestyle decisions can make it difficult if not impossible to pinpoint the most deleterious behaviors using the traditional mechanism of clinical interviews. OBJECTIVE: The aim of this study is to leverage machine learning (ML) algorithms for real-time predictions of stress-induced BP spikes using consumer wearable devices such as Fitbit, providing actionable insights to both patients and clinicians to improve diagnostics and enable proactive health monitoring. This study also seeks to address the significant challenges in identifying specific deleterious behaviors associated with stress-induced hypertension through the development of personalized artificial intelligence models for individual patients, departing from the conventional approach of using generalized models. METHODS: The study proposes the development of ML algorithms to analyze biosignals obtained from these wearable devices, aiming to make real-time predictions about BP spikes. Given the longitudinal nature of the data set comprising time-series data from wearables (eg, Fitbit) and corresponding time-stamped labels representing stress levels from Ecological Momentary Assessment reports, the adoption of self-supervised learning for pretraining the network and using transformer models for fine-tuning the model on a personalized prediction task is proposed. Transformer models, with their self-attention mechanisms, dynamically weigh the importance of different time steps, enabling the model to focus on relevant temporal features and dependencies, facilitating accurate prediction. RESULTS: Supported as a pilot project from the Robert C Perry Fund of the Hawaii Community Foundation, the study team has developed the core study app, CardioMate. CardioMate not only reminds participants to initiate BP readings using an Omron HeartGuide wearable monitor but also prompts them multiple times a day to report stress levels. Additionally, it collects other useful information including medications, environmental conditions, and daily interactions. Through the app's messaging system, efficient contact and interaction between users and study admins ensure smooth progress. CONCLUSIONS: Personalized ML when applied to biosignals offers the potential for real-time digital health interventions for chronic stress and its symptoms. The project's clinical use for Hawaiians with stress-induced high BP combined with its methodological innovation of personalized artificial intelligence models highlights its significance in advancing health care interventions. Through iterative refinement and optimization, the aim is to develop a personalized deep-learning framework capable of accurately predicting stress-induced BP spikes, thereby promoting individual well-being and health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55615.

Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Mobile Intervention to Address Cannabis Use Disorder Among Black Adults: Protocol for a Randomized Controlled Trial.

BACKGROUND: African American or Black (hereafter referred to as Black) adults who use cannabis use it more frequently and are more likely to meet criteria for cannabis use disorder (CUD) than both White and Hispanic or Latin individuals. Black adults may be more apt to use cannabis to cope with distress, which constitutes a false safety behavior (FSB; a behavior designed to reduce psychological distress in the short term). Although FSB engagement can perpetuate the cycle of high rates of CUD among Black individuals, limited work has applied an FSB elimination treatment approach to Black adults with CUD, and no previous work has evaluated FSB reduction or elimination in the context of a culturally tailored and highly accessible treatment developed for Black individuals. OBJECTIVE: This study aims to develop and pilot-test a culturally tailored adaptive intervention that integrates FSB reduction or elimination skills for cannabis reduction or cessation among Black adults with probable CUD (Culturally Tailored-Mobile Integrated Cannabis and Anxiety Reduction Treatment [CT-MICART]). METHODS: Black adults with probable CUD (N=50) will complete a web-based screener, enrollment call, baseline assessment, 3 daily ecological momentary assessments (EMAs) for 6 weeks, and a follow-up self-report assessment and qualitative interview at 6 weeks after randomization. Participants will be randomized into 1 out of the 2 conditions after baseline assessment: (1) CT-MICART+EMAs for 6 weeks or (2) EMAs only for 6 weeks. RESULTS: The enrollment started in June 2023 and ended in November 2023. Data analysis will be completed in March 2024. CONCLUSIONS: No culturally tailored, evidence-based treatment currently caters to the specific needs of Black individuals with CUD. This study will lay the foundation for a new approach to CUD treatment among Black adults that is easily accessible and has the potential to overcome barriers to treatment and reduce practitioner burden in order to support Black individuals who use cannabis with probable CUD. TRIAL REGISTRATION: ClinicalTrials.gov NCT05566730; https://clinicaltrials.gov/study/NCT05566730. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52776.

Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study.

BACKGROUND: Artificial intelligence (AI)-powered digital therapies that detect methamphetamine cravings via consumer devices have the potential to reduce health care disparities by providing remote and accessible care solutions to communities with limited care solutions, such as Native Hawaiian, Filipino, and Pacific Islander communities. However, Native Hawaiian, Filipino, and Pacific Islander communities are understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other racial groups. OBJECTIVE: In this study, we aimed to understand the feasibility of continuous remote digital monitoring and ecological momentary assessments in Native Hawaiian, Filipino, and Pacific Islander communities in Hawaii by curating a novel data set of longitudinal Fitbit (Fitbit Inc) biosignals with the corresponding craving and substance use labels. We also aimed to develop personalized AI models that predict methamphetamine craving events in real time using wearable sensor data. METHODS: We will develop personalized AI and machine learning models for methamphetamine use and craving prediction in 40 individuals from Native Hawaiian, Filipino, and Pacific Islander communities by curating a novel data set of real-time Fitbit biosensor readings and the corresponding participant annotations (ie, raw self-reported substance use data) of their methamphetamine use and cravings. In the process of collecting this data set, we will gain insights into cultural and other human factors that can challenge the proper acquisition of precise annotations. With the resulting data set, we will use self-supervised learning AI approaches, which are a new family of machine learning methods that allows a neural network to be trained without labels by being optimized to make predictions about the data. The inputs to the proposed AI models are Fitbit biosensor readings, and the outputs are predictions of methamphetamine use or craving. This paradigm is gaining increased attention in AI for health care. RESULTS: To date, more than 40 individuals have expressed interest in participating in the study, and we have successfully recruited our first 5 participants with minimal logistical challenges and proper compliance. Several logistical challenges that the research team has encountered so far and the related implications are discussed. CONCLUSIONS: We expect to develop models that significantly outperform traditional supervised methods by finetuning according to the data of a participant. Such methods will enable AI solutions that work with the limited data available from Native Hawaiian, Filipino, and Pacific Islander populations and that are inherently unbiased owing to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46493.

A digital tool for self-assessment of auditory verbal hallucinations in schizophrenia.

Auditory verbal hallucinations (AVH) are experienced by approximately 70 % of patients with schizophrenia and are frequently associated with high levels of distress. Therefore, alleviating hallucinations is an important therapeutic challenge. However, for prescribing a personalized treatment adapted to the patient, an accurate and detailed assessment of AVH is necessary. Until now, there have been no self-evaluations; instead, only scales based on observer ratings have been used to assess AVH. Nevertheless, self-assessments may enhance patient symptom awareness and increase their insight and involvement in the treatment, promoting empowerment (Eisen et al., 2000). In this context, a mobile app called MIMO was devised in order to monitor AVHs assessed by the patients themselves. This app, including the Self-assessment of Auditory verbal Hallucinations (SAVH-https://sns-dollfus.com/), was devised as an ecological momentary assessment tool. The present study aimed to demonstrate the feasibility and acceptability of this app.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study.

BACKGROUND: Increasing efforts toward the prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. Wearable devices have emerged as a possible solution to aid in this process, but their use in real-life stress detection has not been systematically investigated. OBJECTIVE: We aimed to determine the utility of ecological momentary assessments (EMA) and physiological arousal measured through wearable devices in detecting ecologically relevant stress states. METHODS: Using EMA combined with wearable biosensors for ecological physiological assessments (EPA), we investigated the impact of an ecological stressor (ie, a high-stakes examination week) on physiological arousal and affect compared to a control week without examinations in first-year medical and biomedical science students (51/83, 61.4% female). We first used generalized linear mixed-effects models with maximal fitting approaches to investigate the impact of examination periods on subjective stress exposure, mood, and physiological arousal. We then used machine learning models to investigate whether we could use EMA, wearable biosensors, or the combination of both to classify momentary data (ie, beeps) as belonging to examination or control weeks. We tested both individualized models using a leave-one-beep-out approach and group-based models using a leave-one-subject-out approach. RESULTS: During stressful high-stakes examination (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal decreased on average during the examination week. Time-resolved analyses revealed peaks in physiological arousal associated with both momentary self-reported stress exposure and self-reported positive affect. Mediation models revealed that the decreased physiological arousal in the examination week was mediated by lower positive affect during the same period. We then used machine learning to show that while individualized EMA outperformed EPA in its ability to classify beeps as originating from examinations or from control weeks (1603/4793, 33.45% and 1648/4565, 36.11% error rates, respectively), a combination of EMA and EPA yields optimal classification (1363/4565, 29.87% error rate). Finally, when comparing individualized models to group-based models, we found that the individualized models significantly outperformed the group-based models across all 3 inputs (EMA, EPA, and the combination). CONCLUSIONS: This study underscores the potential of wearable biosensors for stress-related mental health monitoring. However, it emphasizes the necessity of psychological context in interpreting physiological arousal captured by these devices, as arousal can be related to both positive and negative contexts. Moreover, our findings support a personalized approach in which momentary stress is optimally detected when referenced against an individual's own data.

The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review.

BACKGROUND: Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data. OBJECTIVE: This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research. METHODS: A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist. RESULTS: After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples. CONCLUSIONS: EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed. TRIAL REGISTRATION: PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.

Describing and visualizing the patient and caregiver experience of cancer pain in the home context using ecological momentary assessments.

Background: Pain continues to be a difficult and pervasive problem for patients with cancer, and those who care for them. Remote health monitoring systems (RHMS), such as the Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), can utilize Ecological Momentary Assessments (EMAs) to provide a more holistic understanding of the patient and family experience of cancer pain within the home context. Methods: Participants used the BESI-C system for 2-weeks which collected data via EMAs deployed on wearable devices (smartwatches) worn by both patients with cancer and their primary family caregiver. We developed three unique EMA schemas that allowed patients and caregivers to describe patient pain events and perceived impact on quality of life from their own perspective. EMA data were analyzed to provide a descriptive summary of pain events and explore different types of data visualizations. Results: Data were collected from five (n = 5) patient-caregiver dyads (total 10 individual participants, 5 patients, 5 caregivers). A total of 283 user-initiated pain event EMAs were recorded (198 by patients; 85 by caregivers) over all 5 deployments with an average severity score of 5.4/10 for patients and 4.6/10 for caregivers' assessments of patient pain. Average self-reported overall distress and pain interference levels (1 = least distress; 4 = most distress) were higher for caregivers (x¯ 3.02, x¯2.60,respectively) compared to patients (x¯ 2.82, x¯ 2.25, respectively) while perceived burden of partner distress was higher for patients (i.e., patients perceived caregivers to be more distressed, x¯ 3.21, than caregivers perceived patients to be distressed, x¯2.55). Data visualizations were created using time wheels, bubble charts, box plots and line graphs to graphically represent EMA findings. Conclusion: Collecting data via EMAs is a viable RHMS strategy to capture longitudinal cancer pain event data from patients and caregivers that can inform personalized pain management and distress-alleviating interventions.

Glucose variability and mood in people with type 1 diabetes using ecological momentary assessment.

OBJECTIVE: Mood fluctuations related to blood glucose excursions are a commonly reported source of diabetes-distress, but research is scarce. We aimed to assess the relationship between real-time glucose variability and mood in adults with type 1 diabetes (T1D) using ecological momentary assessments. METHODS: In this prospective observational study, participants wore a masked continuous glucose monitor and received prompts on their smartphone 6 times a day to answer questions about their current mood (Profile Of Mood States (POMS)-SF (dimensions: Anxiety, Depressive symptoms, Anger, Fatigue, Vigor)) for 14 days. Mixed model analyses examined associations over time between daily Coefficient of Variation (CV) of blood glucose and mean and variability (CV) of POMS scores. Further, within-person differences in sleep and nocturnal hypoglycemia were explored. RESULTS: 18 people with T1D (10 female, mean age 44.3 years) participated. A total of 264 out of 367 days (70.2%) could be included in the analyses. No overall significant associations were found between CV of blood glucose and mean and CV of POMS scores, however, nocturnal hypoglycemia moderated the associations between CV of blood glucose and POMS scales (mean Fatigue Estimate 1.998, p < .006, mean Vigor Estimate -3.308, p < .001; CV Anger Estimate 0.731p = 0.02, CV Vigor Estimate -0.525, p = .006). CONCLUSION: We found no overall relationship between real-time glycemic variability and mood per day. Further research into within-person differences such as sleep and nocturnal hypoglycemia is warranted.

Which symptom to address in psychological treatment for cancer survivors when fear of cancer recurrence, depressive symptoms, and cancer-related fatigue co-occur? Exploring the level of agreement between three systematic approaches to select the focus of treatment.

PURPOSE: To investigate the extent to which three systematic approaches for prioritizing symptoms lead to similar treatment advices in cancer survivors with co-occurring fear of cancer recurrence, depressive symptoms, and/or cancer-related fatigue. METHODS: Psychological treatment advices were was based on three approaches: patient preference, symptom severity, and temporal precedence of symptoms based on ecological momentary assessments. The level of agreement was calculated according to the Kappa statistic. RESULTS: Overall, we found limited agreement between the three approaches. Pairwise comparison showed moderate agreement between patient preference and symptom severity. Most patients preferred treatment for fatigue. Treatment for fear of cancer recurrence was mostly indicated when based on symptom severity. Agreement between temporal precedence and the other approaches was slight. A clear treatment advice based on temporal precedence was possible in 57% of cases. In cases where it was possible, all symptoms were about equally likely to be indicated. CONCLUSIONS: The three approaches lead to different treatment advices. Future research should determine how the approaches are related to treatment outcome. We propose to discuss the results of each approach in a shared decision-making process to make a well-informed and personalized decision with regard to which symptom to target in psychological treatment. IMPLICATIONS FOR CANCER SURVIVORS: This study contributes to the development of systematic approaches for selecting the focus of psychological treatment in cancer survivors with co-occurring symptoms by providing and comparing three different systematic approaches for prioritizing symptoms.

A Mobile Application Adjunct to Augment Cognitive-Behavioral Group Therapy for Adolescents with Social Anxiety: Feasibility and Acceptability Results from the Wiring Adolescents with Social Anxiety via Behavioral Interventions Pilot Trial.

Objective: Cognitive-Behavioral Group Therapy (CBGT) is an established treatment for Social Anxiety (SA). However, diagnostic recovery rate is only 20.5% in CBGT, and up to 50% of patients remain symptomatic posttreatment. Using videocalls to deliver digital CBGT (dCBGT) is feasible, cost-effective, and efficacious. Yet, the impact of dCBGT on social functioning remains limited, as dCBGT does not offer opportunities for monitoring cognition and behavior in social situations. Wiring Adolescents with Social Anxiety via Behavioral Interventions (WASABI), a clinician-assisted application that uses ecological momentary assessments (EMAs), cognitive bias tests, and clinical self-reports, was investigated as an adjunct to dCBGT. Methods: A prospective, parallel arm, double-blind randomized controlled trial was employed in 24 SA adolescents randomly assigned to dCBGT versus dCBGT plus WASABI. Results: Study completion rates (83%) and exit survey data indicated that WASABI is feasible and acceptable. Engagement with EMAs varied from four to 244 EMAs completed per person. Cognitive bias tests and clinical self-reports were completed at least weekly by 53% and 69% of participants, respectively. While standard tests did not reveal statistically significant differences between dCBGT plus WASABI and dCBGT alone, effect sizes were greater for dCBGT plus WASABI on symptom severity, social skills, and functioning. Conclusions: Despite the small sample, preliminary results suggest that WASABI is feasible, acceptable, and may be an effective augmentation tool for treating SA in teenagers.

The way you make me feel: an ecological momentary assessment study on couple similarity in sexual arousal and its sexual, relational, and affective correlates.

BACKGROUND: Although sexual arousal is commonly experienced in the daily context of relationships, most of what we know about sexual arousal comes from studies on individuals, often conducted in a laboratory context. AIM: To explore to what extent similarity in levels of sexual arousal during nongenital physical intimacy (ie, cuddling and kissing) was associated with each partner's affect as well as sexual and relationship satisfaction. METHODS: Ninety-four cohabitating couples (mean ± SD age, 26.30 ± 7.60; 88 mixed gender, 6 same gender) completed 6 ecological momentary assessments a day for 10 consecutive days. We used response surface analysis to examine the associations among the degree and direction of similarity in partners' sexual arousal and affective, sexual, and relational outcome variables. OUTCOMES: Sexual satisfaction, relationship satisfaction, and positive and negative affect. RESULTS: Sexual arousal levels covaried only when partners engaged in physically intimate behaviors, unlike affective responses, which covaried within couples more globally over time. Within-couple similarity at high levels of sexual arousal was positively associated with women's sexual satisfaction but unrelated to men and women's relationship satisfaction and affect. Individual- and couple-level sexual arousal was positively associated with men's sexual satisfaction and women's sexual and relationship satisfaction. Couple-level sexual arousal was relevant to men's affect such that positive affect was higher when sexual arousal levels within the couple were high. Our analyses also revealed a discrepancy effect in that women's positive affect was higher when their own sexual arousal levels were higher than those of their partners. CLINICAL IMPLICATIONS: These findings suggest that as long as sexual arousal levels within a couple are sufficiently high, sexual arousal similarity and discrepancy can be beneficial to one's well-being, supporting the relevance of therapeutical techniques aimed at increasing arousal levels to promote a better affective and relational climate for couples. STRENGTHS AND LIMITATIONS: This study is the first to test the daily associations among sexual arousal similarity and its correlates in a sample of cohabitating couples, providing a more comprehensive view of the interpersonal dynamics through which sexual arousal may influence individual, relational, and sexual well-being. Given our sample's relatively young age, as well as high sexual and relationship satisfaction, the results may not generalize to couples experiencing sexual or relational distress. CONCLUSION: Within the context of daily relationships, individual- and couple-level dynamics of sexual arousal were associated with sexual and relationship satisfaction, as well as with affective responses of relationship partners.

Feasibility and Acceptability of Using Ecological Momentary Assessment to Evaluate Alcohol Use with American Indian Women.

BACKGROUND: Ecological momentary assessments (EMA) are one way to collect timely and accurate alcohol use data, as they involve signaling participants via cell phones to report on daily behaviors in real-time and in a participant's natural environment. EMA has never been used with American Indian populations to evaluate alcohol consumption. The purpose of this project was to determine the feasibility and acceptability of EMA for American Indian women. METHODS: Eligible participants were American Indian women between the ages of 18 and 44 who were not pregnant and had consumed more than one drink within the past month. All participants received a TracFone and weekly automated messages. Self-reported measures of daily quantity and frequency of alcohol consumption, alcohol type, and context were assessed once per week for four weeks. Baseline measurements also included the Drinking Motives Questionnaire-Revised (DMQ-R) and the Interpersonal Support Evaluation List (ISEL). RESULTS: Fifteen participants were enrolled in the study. All but one participant completed all data collection time points, and drinking patterns were consistent across the study period. A total of 420 records were completed across 86 drinking days and 334 non-drinking days. Participants reported drinking an average of 5.7 days over the 30-day period and typically consumed 3.99 drinks per drinking occasion. Sixty-six percent of participants met gender-specific cut-points for heavy episodic drinking, with an average of 2.46 binge drinking occasions across the four week study period. CONCLUSIONS: This proof-of-concept project showed that EMA was both feasible and acceptable for collecting alcohol data from American Indian women. Additional studies are necessary to fully implement EMA with American Indian women to better understand the drinking motives, contexts, patterns, and risk factors in this population.

Linking video chatting, phone calling, text messaging, and social media with peers to adolescent connectedness.

For 14 days three times per day (6072 observations), adolescents (N = 207, Mage = 15.45 years) reported their digital (i.e., video chatting, texting, social media, and phone calling) communication with peers and their social connectedness. Controlling for in-person interactions, adolescents felt more connected in hours when they had communicated with peers by video chatting, texting, or social media, but not phone calling. Girls communicated with peers via text and social media more than boys, and boys talked on the phone more than girls. Boys who talked, texted, or video chatted more on average reported higher connectedness on average, whereas girls did not. As the links with connectedness were only found at the hourly- and not the daily level, results highlight that a sense of connectedness from digital media may be fleeting in nature.

Availability of healthy foods, fruit and vegetable consumption, and cognition among urban older adults.

BACKGROUND: . Although prior studies have examined the associations between neighborhood characteristics and cognitive health, little is known about whether local food environments, which are critical for individuals' daily living, are associated with late-life cognition. Further, little is known about how local environments may shape individuals' health-related behaviors and impact cognitive health. The aim of this study is to examine whether objective and subjective measures of healthy food availability are associated with ambulatory cognitive performance and whether behavioral and cardiovascular factors mediate these associations among urban older adults. METHODS: . The sample consisted of systematically recruited, community-dwelling older adults (N = 315, mean age = 77.5, range = 70-91) from the Einstein Aging Study. Objective availability of healthy foods was defined as density of healthy food stores. Subjective availability of healthy foods and fruit/vegetable consumption were assessed using self-reported questionnaires. Cognitive performance was assessed using smartphone-administered cognitive tasks that measured processing speed, short-term memory binding, and spatial working memory performance 6 times a day for 14 days. RESULTS: . Results from multilevel models showed that subjective availability of healthy foods, but not objective food environments, was associated with better processing speed (estimate= -0.176, p = .003) and more accurate memory binding performance (estimate = 0.042, p = .012). Further, 14~16% of the effects of subjective availability of healthy foods on cognition were mediated through fruit and vegetable consumption. CONCLUSIONS: . Local food environments seem to be important for individuals' dietary behavior and cognitive health. Specifically, subjective measures of food environments may better reflect individuals' experiences regarding their local food environments not captured by objective measures. Future policy and intervention strategies will need to include both objective and subjective food environment measures in identifying impactful target for intervention and evaluating effectiveness of policy changes.

Adaptive data collection for intraindividual studies affected by adherence.

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.

Movement matters: short-term impacts of physical activity on mood and well-being.

Few studies have investigated the short-term, momentary relationships between physical activity (PA) and well-being. This study focuses on investigating the dynamic relationships between PA and affective well-being among adults with type 1 diabetes. Participants (n = 122) wore an accelerometer and completed daily EMA surveys of current activities and affective states (e.g., happy, stressed, excited, anxious) via smartphone over 14 days. Within-person, increased sedentary time was associated with less positive affect (r = - 0.11, p < 0.001), while more PA of any intensity was associated with greater positive affect and reduced fatigue, three hours later. Between-person, increased light PA was associated with increased stress (r = 0.21, p = 0.02) and diabetes distress (r = 0.30, p = 0.001). This study provides evidence that positive affect and fatigue are predicted by previous activity regardless of the different activities that people engaged in. Positive affect increased after engaging in PA. However, participants with higher amounts of light PA reported higher stress ratings.