Eldin Dzubur

Background: Evidence-based digital health programs have shown efficacy in being primary tools to improve emotional and mental health, as well as offering supplementary support to individuals undergoing psychotherapy for anxiety, depression, and other mental health disorders. However, information is lacking about the dose response to digital mental health interventions. Objective: The objective of the study was to examine the effect of time in program and program usage on symptom change among individuals enrolled in a real-world comprehensive digital mental health program (myStrength) who are experiencing severe anxiety or depression. Methods: Eligible participants (N=18,626) were adults aged 18 years and older who were enrolled in myStrength for at least four weeks as part of their employee wellness benefit program, who completed baseline, the 2-week, 2-month, and 6-month surveys querying symptoms of anxiety (Generalized Anxiety Disorder–7 [GAD-7]) and depression (Patient Health Questionnaire–9 [PHQ-9]). Linear growth curve models were used to analyze the effect of average weekly program usage on subsequent GAD-7 and PHQ-9 scores for participants with scores indicating severe anxiety (GAD-7≥15) or depression (PHQ-9≥15). All models were adjusted for baseline score and demographics. Results: Participants in the study (N=1519) were 77.4% female (1176/1519), had a mean age of 45 years (SD 14 years), and had an average enrollment time of 3 months. At baseline, participants reported an average of 9.39 (SD 6.04) on the GAD-7 and 11.0 (SD 6.6) on the PHQ-9. Those who reported 6-month results had an average of 8.18 (SD 6.15) on the GAD-7 and 9.18 (SD 6.79) on the PHQ-9. Participants with severe scores (n=506) experienced a significant improvement of 2.97 (SE 0.35) and 3.97 (SE 0.46) at each time point for anxiety and depression, respectively (t=–8.53 and t=–8.69, respectively; Ps<.001). Those with severe baseline scores also saw a reduction of 0.27 (SE 0.08) and 0.25 (SE 0.09) points in anxiety and depression, respectively, for each additional program activity per week (t=–3.47 and t=–2.66, respectively; Ps<.05). Conclusions: For participants with severe baseline scores, the study found a clinically significant reduction of approximately 9 points for anxiety and 12 points for depression after 6 months of enrollment, suggesting that interventions targeting mental health must maintain active, ongoing engagement when symptoms are present and be available as a continuous resource to maximize clinical impact, specifically in those experiencing severe anxiety or depression. Moreover, a dosing effect was shown, indicating improvement in outcomes among participants who engaged with the program every other day for both anxiety and depression. This suggests that digital mental health programs that provide both interesting and evidence-based activities could be more successful in further improving mental health outcomes.

Background: Compared to the effects of stress on hunger, the temporal effect of hunger on stress levels is less understood, especially in the context of everyday lives of vulnerable populations with unstable access to food. Objective: Our objective was to examine the effects of food insecurity and momentary hunger on momentary stress and stress variability in a sample of currently and formerly homeless young adults. Method: We used a 7-day ecological momentary assessment study querying affect, hunger, and risky behaviors. A mixed-effects location scale model was used to examine the effects of hunger on mean levels and within- and between-subjects variability of stress with 100 currently homeless and 69 formerly homeless young adults ages 18-29 in Los Angeles County, California. Results: When individuals experienced greater-than-average hunger, they then experienced greater stress variability at the next prompt, showing the impact of hunger on stress at the momentary level. Those with higher average levels of stress, regardless of hunger, became substantially more stressed when becoming hungry compared to their generally less stressed counterparts. Conclusions: The study shows the extent to which food insecurity results in erratic stress among vulnerable populations and how high levels of hunger may lead to a more inconsistent stress response. Findings reinforce the need for more mental health services and food programs for young adults who have experienced homelessness.

Background: Ecological momentary assessment (EMA) has been used with young people experiencing homelessness to gather information on contexts associated with homelessness and risk behavior in real time and has proven feasible in this population. However, the extent to which EMA may affect the attitudes or behaviors of young adults who are currently or were formerly homeless and are residing in supportive housing has not been well investigated. Objective: This study aims to describe the feedback regarding EMA study participation from young adults who are currently or were formerly homeless and examine the reactivity to EMA participation and compliance. Methods: This mixed methods study used cross-sectional data collected before and after EMA, intensive longitudinal data from a 7-day EMA prompting period, and focus groups of young adults who are currently or were formerly homeless in Los Angeles, California, between 2017 and 2019. Results: Qualitative data confirmed the quantitative findings. Differences in the experience of EMA between young adults who are currently or were formerly homeless were found to be related to stress or anxiety, interference with daily life, difficulty charging, behavior change, and honesty in responses. Anxiety and depression symptomatology decreased from before to after EMA; however, compliance was not significantly associated with this decrease. Conclusions: The results point to special considerations when administering EMA to young adults who are currently or were formerly homeless. EMA appears to be slightly more burdensome for young adults who are currently homeless than for those residing in supportive housing, which are nuances to consider in the study design. The lack of a relationship between study compliance and symptomatology suggests low levels of reactivity.

IntroductionLittle is known about the momentary patterns and predictors of substance use among young adults who experience homelessness. To enhance understanding of substance use patterns, smartphone-based ecological momentary assessment (EMA) was utilized to examine the real-time association between affect and substance use. Methods: 251 young adults (aged 18–27) with history of homelessness were recruited from supportive housing programs and drop-in facilities in Los Angeles. Exploratory factor analysis was used to examine the latent structure of positive and negative affective states and mixed-effects logistic regression models were completed separately for both the full remaining sample (n = 227) and a subsample of alcohol or cannabis users (n = 145) to evaluate whether positive or negative affect predicted lead, recent, or lagged substance use. Results: Greater positive affect within-person was associated with greater odds of alcohol or cannabis use within the past two hours, and participants who reported feeling more negative than their peers experienced greater odds of reporting use within the past 4 h and the following two hours. Conclusion: Results suggest that individuals experience a heightened positive mood compared to their own average mood, concurrently or immediately after engaging in alcohol or cannabis use. Heightened positive mood might be an anticipatory effect of drinking or cannabis use. Future research should consider a longer study period to capture multiple drinking or drug use events over a longer period and consider more environmental exposures that may influence the frequency or intensity of substance use.

BACKGROUND Evidence-based digital health programs have shown efficacy in being primary tools to improve emotional and mental health, as well as offering supplementary support to individuals undergoing psychotherapy for anxiety, depression, and other mental health disorders. However, information is lacking about the dose response to digital mental health interventions. OBJECTIVE The objective of the study was to examine the effect of time in program and program usage on symptom change among individuals enrolled in a real-world comprehensive digital mental health program (myStrength) who are experiencing severe anxiety or depression. METHODS Eligible participants (N=18,626) were adults aged 18 years and older who were enrolled in myStrength for at least four weeks as part of their employee wellness benefit program, who completed baseline, the 2-week, 2-month, and 6-month surveys querying symptoms of anxiety (Generalized Anxiety Disorder–7 [GAD-7]) and depression (Patient Health Questionnaire–9 [PHQ-9]). Linear growth curve models were used to analyze the effect of average weekly program usage on subsequent GAD-7 and PHQ-9 scores for participants with scores indicating severe anxiety (GAD-7≥15) or depression (PHQ-9≥15). All models were adjusted for baseline score and demographics. RESULTS Participants in the study (N=1519) were 77.4% female (1176/1519), had a mean age of 45 years (SD 14 years), and had an average enrollment time of 3 months. At baseline, participants reported an average of 9.39 (SD 6.04) on the GAD-7 and 11.0 (SD 6.6) on the PHQ-9. Those who reported 6-month results had an average of 8.18 (SD 6.15) on the GAD-7 and 9.18 (SD 6.79) on the PHQ-9. Participants with severe scores (n=506) experienced a significant improvement of 2.97 (SE 0.35) and 3.97 (SE 0.46) at each time point for anxiety and depression, respectively (t=–8.53 and t=–8.69, respectively; Ps<.001). Those with severe baseline scores also saw a reduction of 0.27 (SE 0.08) and 0.25 (SE 0.09) points in anxiety and depression, respectively, for each additional program activity per week (t=–3.47 and t=–2.66, respectively; Ps<.05). CONCLUSIONS For participants with severe baseline scores, the study found a clinically significant reduction of approximately 9 points for anxiety and 12 points for depression after 6 months of enrollment, suggesting that interventions targeting mental health must maintain active, ongoing engagement when symptoms are present and be available as a continuous resource to maximize clinical impact, specifically in those experiencing severe anxiety or depression. Moreover, a dosing effect was shown, indicating improvement in outcomes among participants who engaged with the program every other day for both anxiety and depression. This suggests that digital mental health programs that provide both interesting and evidence-based activities could be more successful in further improving mental health outcomes.

BACKGROUND Ecological momentary assessment (EMA) has been used with young people experiencing homelessness to gather information on contexts associated with homelessness and risk behavior in real time and has proven feasible in this population. However, the extent to which EMA may affect the attitudes or behaviors of young adults who are currently or were formerly homeless and are residing in supportive housing has not been well investigated. OBJECTIVE This study aims to describe the feedback regarding EMA study participation from young adults who are currently or were formerly homeless and examine the reactivity to EMA participation and compliance. METHODS This mixed methods study used cross-sectional data collected before and after EMA, intensive longitudinal data from a 7-day EMA prompting period, and focus groups of young adults who are currently or were formerly homeless in Los Angeles, California, between 2017 and 2019. RESULTS Qualitative data confirmed the quantitative findings. Differences in the experience of EMA between young adults who are currently or were formerly homeless were found to be related to stress or anxiety, interference with daily life, difficulty charging, behavior change, and honesty in responses. Anxiety and depression symptomatology decreased from before to after EMA; however, compliance was not significantly associated with this decrease. CONCLUSIONS The results point to special considerations when administering EMA to young adults who are currently or were formerly homeless. EMA appears to be slightly more burdensome for young adults who are currently homeless than for those residing in supportive housing, which are nuances to consider in the study design. The lack of a relationship between study compliance and symptomatology suggests low levels of reactivity. CLINICALTRIAL

Background: Proactive detection of mental health needs among people with diabetes mellitus could facilitate early intervention, improve overall health and quality of life, and reduce individual and societal health and economic burdens. Passive sensing and ecological momentary assessment are relatively newer methods that may be leveraged for such proactive detection. Objective: The primary aim of this study was to conceptualize, develop, and evaluate a novel machine learning approach for predicting mental health risk in people with diabetes mellitus. Methods: A retrospective study was designed to develop and evaluate a machine learning model, utilizing data collected from 142,432 individuals with diabetes enrolled in the Livongo for Diabetes program. First, participants’ mental health statuses were verified using prescription and medical and pharmacy claims data. Next, four categories of passive sensing signals were extracted from the participants’ behavior in the program, including demographics and glucometer, coaching, and event data. Data sets were then assembled to create participant-period instances, and descriptive analyses were conducted to understand the correlation between mental health status and passive sensing signals. Passive sensing signals were then entered into the model to train and test its performance. The model was evaluated based on seven measures: sensitivity, specificity, precision, area under the curve, F1 score, accuracy, and confusion matrix. SHapley Additive exPlanations (SHAP) values were computed to determine the importance of individual signals. Results: In the training (and validation) and three subsequent test sets, the model achieved a confidence score greater than 0.5 for sensitivity, specificity, area under the curve, and accuracy. Signals identified as important by SHAP values included demographics such as race and gender, participant’s emotional state during blood glucose checks, time of day of blood glucose checks, blood glucose values, and interaction with the Livongo mobile app and web platform. Conclusions: Results of this study demonstrate the utility of a passively informed mental health risk algorithm and invite further exploration to identify additional signals and determine when and where such algorithms should be deployed.

Background: Adults experiencing homelessness have a high burden of sleep disturbance, which may be reduced by accessing permanent supportive housing. Objectives: To assess sleep disturbances and their correlates, including demographics, activity level, health status, age-related health issues (eg, functionality and cognitive impairment), substance use, and homelessness history in a sample of permanent supportive housing (PSH) tenants. Research Design: Cross-sectional survey design. Subjects: A total of 237 formerly homeless adults between 45 and 80 years old. Measures: The Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance short form was used to measure sleep disturbance. Results: Twenty-eight percent of our sample had PROMIS scores indicative of a moderate or severe sleep disturbance. Functional impairment, pain, and mental health comorbidities were associated with increased sleep disturbance in multivariable linear regression analyses. The number of years a person experienced homelessness was inversely associated with sleep disturbance. Conclusions: This study supports the need to screen for sleep disturbances among PSH tenants. The findings suggest that supportive services in PSH may need to include integrated physical and behavioral health care, pain management, and interventions designed to address activities of daily livings to improve tenant sleep. They also suggest that improved sleep may help reduce PSH tenant pain, impairment, and mental health symptoms among PSH tenants.

BACKGROUND Proactive detection of mental health needs among people with diabetes mellitus could facilitate early intervention, improve overall health and quality of life, and reduce individual and societal health and economic burdens. Passive sensing and ecological momentary assessment are relatively newer methods that may be leveraged for such proactive detection. OBJECTIVE The primary aim of this study was to conceptualize, develop, and evaluate a novel machine learning approach for predicting mental health risk in people with diabetes mellitus. METHODS A retrospective study was designed to develop and evaluate a machine learning model, utilizing data collected from 142,432 individuals with diabetes enrolled in the Livongo for Diabetes program. First, participants’ mental health statuses were verified using prescription and medical and pharmacy claims data. Next, four categories of passive sensing signals were extracted from the participants’ behavior in the program, including demographics and glucometer, coaching, and event data. Data sets were then assembled to create participant-period instances, and descriptive analyses were conducted to understand the correlation between mental health status and passive sensing signals. Passive sensing signals were then entered into the model to train and test its performance. The model was evaluated based on seven measures: sensitivity, specificity, precision, area under the curve, F1 score, accuracy, and confusion matrix. SHapley Additive exPlanations (SHAP) values were computed to determine the importance of individual signals. RESULTS In the training (and validation) and three subsequent test sets, the model achieved a confidence score greater than 0.5 for sensitivity, specificity, area under the curve, and accuracy. Signals identified as important by SHAP values included demographics such as race and gender, participant’s emotional state during blood glucose checks, time of day of blood glucose checks, blood glucose values, and interaction with the Livongo mobile app and web platform. CONCLUSIONS Results of this study demonstrate the utility of a passively informed mental health risk algorithm and invite further exploration to identify additional signals and determine when and where such algorithms should be deployed.