Mobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the ...impact they have on mental health outcomes.
The objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self-guided mobile apps for depression.
Participants were recruited through Web-based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant's smartphone or tablet with minimal contact with study staff. We enrolled 626 English-speaking adults (≥18 years old) with mild to moderate depression as determined by a 9-item Patient Health Questionnaire (PHQ-9) score ≥5, or if their score on item 10 was ≥2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence-based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ-9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed.
We randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ-9 score >10 (moderately depressed). Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ-9 score >10, with the cognitive training and problem-solving apps resulting in greater effects on mood than the information control app (χ22=6.46, P=.04).
Mobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression.
Clinicaltrials.gov NCT00540865; https://www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http://www.webcitation.org/6mj8IPqQr).
The technology for evaluating patient-provider interactions in psychotherapy-observational coding-has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in ...evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies.
Marital and family researchers often study infrequent behaviors. These powerful psychological variables, such as abuse, criticism, and drug use, have important ramifications for families and society ...as well as for the statistical models used to study them. Most researchers continue to rely on ordinary least-squares (OLS) regression for these types of data, but estimates and inferences from OLS regression can be seriously biased for count data such as these. This article presents a tutorial on statistical methods for positively skewed event data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. These statistical methods are introduced through a marital commitment example, and the data and computer code to run the example analyses in R, SAS, SPSS, and Mplus are included in the online supplemental material. Extensions and practical advice are given to assist researchers in using these tools with their data.
During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., 'displays warmth and confidence', or 'attempts to set up collaboration') ...to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS.
Releasing T cells from inhibitory control has been a strategy exploited by the anti–CTLA-4 antibody ipilimumab. Now an antibody against a second checkpoint molecule, programmed death 1 (PD-1), has ...also shown activity against cancers, including non–small-cell lung cancer.
Human cancers harbor numerous genetic and epigenetic alterations, generating neoantigens that are potentially recognizable by the immune system.
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Although an endogenous immune response to cancer is observed in preclinical models and patients, this response is ineffective, because tumors develop multiple resistance mechanisms, including local immune suppression, induction of tolerance, and systemic dysfunction in T-cell signaling.
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Moreover, tumors may exploit several distinct pathways to actively evade immune destruction, including endogenous “immune checkpoints” that normally terminate immune responses after antigen activation. These observations have resulted in intensive efforts to develop immunotherapeutic approaches for cancer, including immune-checkpoint-pathway inhibitors such as anti–CTLA-4 antibody . . .
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•Mild Parkinson’s can be distinguished from no Parkinson’s using voice features.•Identity confounding leads to overestimates of model performance.•Gradient boosted model outperforms ...Random Forest and Logistic Regression models.
Voice technology has grown tremendously in recent years and using voice as a biomarker has also been gaining evidence. We demonstrate the potential of voice in serving as a deep phenotype for Parkinson’s Disease (PD), the second most common neurodegenerative disorder worldwide, by presenting methodology for voice signal processing for clinical analysis. Detection of PD symptoms typically requires an exam by a movement disorder specialist and can be hard to access and inconsistent in findings. A vocal digital biomarker could supplement the cumbersome existing manual exam by detecting and quantifying symptoms to guide treatment. Specifically, vocal biomarkers of PD are a potentially effective method of assessing symptoms and severity in daily life, which is the focus of the current research. We analyzed a database of PD patient and non-PD subjects containing voice recordings that were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. The results are presented here and the limitations are discussed given the nature of the recordings. We note that our methodology only advances biomarker research and is not cleared for clinical use. Specifically, we demonstrate that conventional machine learning models applied to voice signals can be used to differentiate participants with PD who exhibit little to no symptoms from healthy controls. This work highlights the potential of voice to be used for early detection of PD and indicates that voice may serve as a deep phenotype for PD, enabling precision medicine by improving the speed, accuracy, accessibility, and cost of PD management.
Abstract Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters ...to code therapy sessions, which requires considerable time, training, and financial costs. Natural language processing techniques have recently been utilized for coding MI sessions using machine learning techniques, rather than human coders, and preliminary results have suggested these methods hold promise. The current study extends this previous work by introducing two natural language processing models for automatically coding MI sessions via computer. The two models differ in the way they semantically represent session content, utilizing either 1) simple discrete sentence features (DSF model) and 2) more complex recursive neural networks (RNN model). Utterance- and session-level predictions from these models were compared to ratings provided by human coders using a large sample of MI sessions ( N = 341 sessions; 78,977 clinician and client talk turns) from 6 MI studies. Results show that the DSF model generally had slightly better performance compared to the RNN model. The DSF model had “good” or higher utterance-level agreement with human coders (Cohen's kappa > 0.60) for open and closed questions, affirm, giving information, and follow/neutral (all therapist codes); considerably higher agreement was obtained for session-level indices, and many estimates were competitive with human-to-human agreement. However, there was poor agreement for client change talk, client sustain talk, and therapist MI-inconsistent behaviors. Natural language processing methods provide accurate representations of human derived behavioral codes and could offer substantial improvements to the efficiency and scale in which MI mechanisms of change research and fidelity monitoring are conducted.
Background
Smartphones provide a low‐cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone‐based sensor ...and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood.
Method
Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire‐2. A machine learning approach was used to predict daily mood for the entire sample and individual participants.
Results
Sample‐wide estimates showed a marginally significant association between physical mobility and self‐reported daily mood (B = –0.04, P < 0.05), but the predictive models performed poorly for the sample as a whole (median R2 ∼ 0). Focusing on individuals, 13.9% of participants showed significant association (FDR < 0.10) between a passive feature and daily mood. Personalized models combining features provided better prediction performance (median area under the curve AUC > 0.50) for 80.6% of participants and very strong prediction in a subset (median AUC > 0.80) for 11.8% of participants.
Conclusions
Passive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra‐ and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS‐derived mobility being the top most important feature in the present sample.
Depression is an important precursor to dementia, but less is known about the role dementia plays in altering the course of depression. We examined whether depression prevalence, incidence, and ...severity are higher in those with dementia versus those with mild cognitive impairment (MCI), or normal cognition.
Prospective cohort study using the longitudinal Uniform Data Set of the National Alzheimer's Coordinating Center (2005-2013).
34 Alzheimer Disease research centers.
27,776 subjects with dementia, MCI, or normal cognition.
Depression status was determined by a clinical diagnosis of depression within the prior 2 years and by a Geriatric Depression Scale-Short Form score >5.
Rates of depression were significantly higher in subjects with MCI and dementia compared with those with normal cognition at index visit. Controlling for demographics and common chronic conditions, logistic regression analysis revealed elevated depression in those with MCI (OR: 2.40 95% CI: 2.25, 2.56) or dementia (OR: 2.64 95% CI: 2.43, 2.86) relative to those with normal cognition. In the subjects without depression at the index visit (N = 18,842), those with MCI and dementia had higher probabilities of depression diagnosis 2 years post index visit than those with normal cognition: MCI = 21.7%, dementia = 24.7%, normal cognition = 10.5%.
MCI and dementia were associated with significantly higher rates of depression in concurrent as well as prospective analyses. These findings suggest that efforts to effectively engage and treat older adults with dementia will need also to address co-occurring depression.
IMPORTANCE: Accessible and cost-effective interventions for suicidality are needed to address high rates of suicidal behavior among military service members. Caring Contacts are brief periodic ...messages that express unconditional care and concern and have been previously shown to prevent suicide deaths, attempts, ideation, and hospitalizations. OBJECTIVE: To test the effectiveness of augmenting standard military health care with Caring Contacts delivered via text message to reduce suicidal thoughts and behaviors over 12 months. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was conducted at 3 military installations in the southern and western United States. Soldiers and Marines identified as being at risk of suicide were recruited between April 2013 and September 2016. The final follow-up was in September 2017. INTERVENTIONS: Both groups received standard care, and the Caring Contacts group also received consisted of 11 text messages delivered on day 1, at week 1, at months 1, 2, 3, 4, 6, 8, 10, and 12, and on participants’ birthdays. MAIN OUTCOMES AND MEASURES: Primary outcomes were current suicidal ideation and suicide risk incidents (hospitalization or medical evacuation). Secondary outcomes were worst-point suicidal ideation, emergency department visits, and suicide attempts. Suicidal ideation was measured by the Scale for Suicide Ideation, suicide risk incidents, and emergency department visits by the Treatment History Interview; attempted suicide was measured by the Suicide Attempt Self-Injury Count. RESULTS: Among 658 randomized participants (329 randomizely assigned to each group), data were analyzed for 657 individuals (mean SD age, 25.2 6.1 years; 539 men 82.0%). All participants reported suicidal ideation at baseline, and 291 (44.3%) had previously attempted suicide. Of the 657 participants, 461 (70.2%) were assessed at 12 months. Primary outcomes were nonsignificant. There was no significant effect on likelihood or severity of current suicidal ideation or likelihood of a suicide risk incident; there was also no effect on emergency department visits. However, participants who received Caring Contacts (172 of 216 participants 79.6%) had lower odds than those receiving standard care alone (179 of 204 participants 87.7%) of experiencing any suicidal ideation between baseline and follow-up (odds ratio, 0.56 95% CI, 0.33-0.95; P = .03) and fewer had attempted suicide since baseline (21 of 233 9.0% in the group receiving Caring Contacts vs 34 of 228 14.9% in the standard-care group; odds ratio, 0.52 95% CI, 0.29-0.92; P = .03). CONCLUSIONS AND RELEVANCE: This trial provides inconsistent results on the effectiveness of caring text messages between primary and secondary outcomes, but this inexpensive and scalable intervention offers promise for preventing suicide attempts and ideation in military personnel. Additional research is needed. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01829620
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