Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students.

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

Combining digital pill and smartphone data to quantify medication adherence in an observational psychiatric pilot study.

Digital medicine systems (DMSs) offer a potential solution to increase medication adherence, which is an important barrier to treatment of psychiatric disorders. In this pilot, we enrolled N = 24 individuals diagnosed with severe mental illness to use an FDA-approved DMS for 5 months. We also collected digital phenotyping smartphone data to study behavioral associations with medication adherence. Our results suggest it is feasible to use the system, and we identified longitudinal associations between adherence and some of the communication-based phenotyping features. Larger studies and a focus on data quality are important next steps for this work.

Coaching to Support Mental Health Apps: Exploratory Narrative Review.

BACKGROUND: The therapeutic alliance is crucial for the success of face-to-face therapies. Little is known about how coaching functions and fosters the therapeutic alliance in asynchronous treatment modalities such as smartphone apps. OBJECTIVE: The aim of this paper was to assess how coaching functions and fosters the therapeutic alliance in asynchronous treatment modalities. METHODS: We conducted a selected review to gather preliminary data about the role of coaching in mobile technology use for mental health care. We identified 26 trials using a 2019 review by Tønning et al and a 2021 scoping review by Tokgöz et al to assess how coaching is currently being used across different studies. RESULTS: Our results showed a high level of heterogeneity as studies used varying types of coaching methods but provided little information about coaching protocols and training. Coaching was feasible by clinicians and nonclinicians, scheduled and on demand, and across all technologies ranging from phone calls to social media. CONCLUSIONS: Further research is required to better understand the effects of coaching in mobile mental health treatments, but examples offered from reviewed papers suggest several options to implement coaching today. Coaching based on replicable protocols that are verifiable for fidelity will enable the scaling of this model and a better exploration of the digital therapeutic alliance.

Practices for monitoring and responding to incoming data on self-injurious thoughts and behaviors in intensive longitudinal studies: A systematic review.

Advancements in the understanding and prevention of self-injurious thoughts and behaviors (SITBs) are urgently needed. Intensive longitudinal data collection methods-such as ecological momentary assessment-capture fine-grained, "real-world" information about SITBs as they occur and thus have the potential to narrow this gap. However, collecting real-time data on SITBs presents complex ethical and practical considerations, including about whether and how to monitor and respond to incoming information about SITBs from suicidal or self-injuring individuals during the study. We conducted a systematic review of protocols for monitoring and responding to incoming data in previous and ongoing intensive longitudinal studies of SITBs. Across the 61 included unique studies/samples, there was no clear most common approach to managing these ethical and safety considerations. For example, studies were fairly evenly split between either using automated notifications triggered by specific survey responses (e.g., indicating current suicide risk) or monitoring and intervening upon (generally with a phone-based risk assessment) incoming responses (36%), using both automated notifications and monitoring/intervening (35%), or neither using automated notifications nor monitoring/intervening (29%). Certain study characteristics appeared to influence the safety practices used. Future research that systematically evaluates optimal, feasible strategies for managing risk in real-time monitoring research on SITBs is needed.

Deriving symptom networks from digital phenotyping data in serious mental illness.

BACKGROUND: Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms. AIMS: In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis. METHOD: Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains. RESULTS: The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups. CONCLUSIONS: Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Digital Health Around Clinical High Risk and First-Episode Psychosis.

PURPOSE OF REVIEW: This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling. RECENT FINDINGS: There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care.

The digital clinic: Implementing technology and augmenting care for mental health.

Increasing both access to and quality of mental healthcare is a global priority. One solution is to integrate technologies such as smartphone apps and sensors directly into care. Acknowledging many prior attempts and barriers, we introduce the Digital Clinic which is an already functioning clinic using smartphone apps to augment and extend care today at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts. In this piece, we outline the theoretical foundation of the Digital Clinic and its emphasis on the therapeutic alliance, measurement-based care, and shared decision making. We explore both workflow and engagement challenges as well as solutions including a new care team member, the Digital Navigator, and the customization of technology. Acknowledging that the Digital Clinic is an evolving program, we offer details on our implementation in order to allow others to replicate, expand on, and improve these initial efforts.

Technology Evaluation and Assessment Criteria for Health Apps (TEACH-Apps): Pilot Study.

BACKGROUND: Despite the emergence of app evaluation tools, there remains no well-defined process receptive to diverse local needs, rigorous standards, and current content. The need for such a process to assist in the implementation of app evaluation across all medical fields is evident. Such a process has the potential to increase stakeholder engagement and catalyze interest and engagement with present-day app evaluation models. OBJECTIVE: This study aimed to develop and pilot test the Technology Evaluation and Assessment Criteria for Health apps (TEACH-apps). METHODS: Tailoring a well-known implementation framework, Replicating Effective Programs, we present a new process to approach the challenges faced in implementing app evaluation tools today. As a culmination of our experience implementing this process and feedback from stakeholders, we present the four-part process to aid the implementation of mobile health technology. This paper outlines the theory, evidence, and initial versions of the process. RESULTS: The TEACH-apps process is designed to be broadly usable and widely applicable across all fields of health. The process comprises four parts: (1) preconditions (eg, gathering apps and considering local needs), (2) preimplementation (eg, customizing criteria and offering digital skills training), (3) implementation (eg, evaluating apps and creating educational handouts), and (4) maintenance and evolution (eg, repeating the process every 90 days and updating content). TEACH-apps has been tested internally at our hospital, and there is growing interest in partnering health care facilities to test the system at their sites. CONCLUSIONS: This implementation framework introduces a process that equips stakeholders, clinicians, and users with the foundational tools to make informed decisions around app use and increase app evaluation engagement. The application of this process may lead to the selection of more culturally appropriate and clinically relevant tools in health care.

Longitudinal trends in the quality, effectiveness and attributes of highly rated smartphone health apps.

BACKGROUND: While there are numerous mental health apps on the market today, less is known about their safety and quality. This study aims to offer a longitudinal perspective on the nature of high visibility apps for common mental health and physical health conditions. METHODS: In July 2019, we selected the 10 top search-returned apps in the Apple App Store and Android Google Play Store using six keyword terms: depression, anxiety, schizophrenia, addiction, high blood pressure and diabetes. Each app was downloaded by two authors and reviewed by a clinician, and the app was coded for features, functionality, claims, app store properties, and other properties. RESULTS: Compared with 1 year prior, there were few statistically significant changes in app privacy policies, evidence and features. However, there was a high rate of turnover with only 34 (57%) of the apps from the Apple's App Store and 28 (47%) from the Google Play Store remaining in the 2019 top 10 search compared with the 2018 search. DISCUSSION: Although there was a high turnover of top search-returned apps between 2018 and 2019, we found that there were few significant changes in features, privacy, medical claims and other properties. This suggests that, although the highly visible and available apps are changing, there were no significant improvements in app quality or safety.

Digital Opportunities for Outcomes in Recovery Services (DOORS): A Pragmatic Hands-On Group Approach Toward Increasing Digital Health and Smartphone Competencies, Autonomy, Relatedness, and Alliance for Those With Serious Mental Illness.

Digital health technologies such as smartphones present the potential for increased access to care and on-demand services. However, many patients with serious mental illnesses (eg, schizophrenia) have not been offered the digital health training necessary to fully utilize these innovative approaches. To bridge this digital divide in knowledge and skills, we created a hands-on and interactive training program grounded in self-determination theory, technology use cases, and the therapeutic alliance. This article introduces the need and theoretical foundation for and the experience of running the resulting Digital Opportunities for Outcomes in Recovery Services (DOORS) group in the setting of 2 programs: a first-episode psychosis program and a clubhouse for individuals with serious mental illness. The experience of running these 2 DOORS groups resulted in 2 publicly available, free training manuals to empower others to run such groups and adapt them for local needs. Future work on DOORS will expand the curriculum to best support digital health needs and increase equity of access to and knowledge and skills related to technology use in serious mental illness.

The Role of Digital Navigators in Promoting Clinical Care and Technology Integration into Practice.

As the role of technology expands in healthcare, so does the need to support its implementation and integration into the clinic. The concept of a new team member, the digital navigator, able to assume this role is introduced as a solution. With a digital navigator, any clinic today can take advantage of digital health and smartphone tools to augment and expand existing telehealth and face to face care. The role of a digital navigator is suitable as an entry level healthcare role, additional training for an experienced clinician, and well suited to peer specialists. To facilitate the training of digital navigators, we draw upon our experience in creating the role and across health education to introduce a 10-h curriculum designed to train digital navigators across 5 domains: (1) core smartphone skills, (2) basic technology troubleshooting, (3) app evaluation, (4) clinical terminology and data, and (5) engagement techniques. This paper outlines the curricular content, skills, and modules for this training and features a rich online supplementary Appendix with step by step instructions and resources.

Assessing Cognition Outside of the Clinic: Smartphones and Sensors for Cognitive Assessment Across Diverse Psychiatric Disorders.

Traditionally, the assessment of cognition and the administration of cognitive therapies has been performed in the clinic, but with modern technology, this clinic-centric view is changing. This article explores the landscape of digital devices used to measure cognition in settings outside the clinic. These devices range in mobility from user-friendly mobile devices to setting-specific devices able to provide powerful, robust cognitive therapy and living assistance in the comfort of a patient's home. Although these methods remain in early stages of developmental, initial studies suggest they may prove useful in treating patients with serious mental illnesses in a widespread clinical setting.

Using a Smartphone App to Identify Clinically Relevant Behavior Trends via Symptom Report, Cognition Scores, and Exercise Levels: A Case Series.

The use of smartphone apps for research and clinical care in mental health has become increasingly popular, especially within youth mental health. In particular, digital phenotyping, the monitoring of data streams from a smartphone to identify proxies for functional outcomes like steps, sleep, and sociability, is of interest due to the ability to monitor these multiple relevant indications of clinically symptomatic behavior. However, scientific progress in this field has been slow due to high heterogeneity among smartphone apps and lack of reproducibility. In this paper, we discuss how our division utilized a smartphone app to retrospectively identify clinically relevant behaviors in individuals with psychosis by measuring survey scores (symptom report), games (cognition scores), and step count (exercise levels). Further, we present specific cases of individuals and how the relevance of these data streams varied between them. We found that there was high variability between participants and that each individual's relevant behavior patterns relied heavily on unique data streams. This suggests that digital phenotyping has high potential to augment clinical care, as it could provide an efficient and individualized mechanism of identifying relevant clinical implications even if population-level models are not yet possible.

Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape.

OBJECTIVE: The aim of this review was to explore the current evidence for conversational agents or chatbots in the field of psychiatry and their role in screening, diagnosis, and treatment of mental illnesses. METHODS: A systematic literature search in June 2018 was conducted in PubMed, EmBase, PsycINFO, Cochrane, Web of Science, and IEEE Xplore. Studies were included that involved a chatbot in a mental health setting focusing on populations with or at high risk of developing depression, anxiety, schizophrenia, bipolar, and substance abuse disorders. RESULTS: From the selected databases, 1466 records were retrieved and 8 studies met the inclusion criteria. Two additional studies were included from reference list screening for a total of 10 included studies. Overall, potential for conversational agents in psychiatric use was reported to be high across all studies. In particular, conversational agents showed potential for benefit in psychoeducation and self-adherence. In addition, satisfaction rating of chatbots was high across all studies, suggesting that they would be an effective and enjoyable tool in psychiatric treatment. CONCLUSION: Preliminary evidence for psychiatric use of chatbots is favourable. However, given the heterogeneity of the reviewed studies, further research with standardized outcomes reporting is required to more thoroughly examine the effectiveness of conversational agents. Regardless, early evidence shows that with the proper approach and research, the mental health field could use conversational agents in psychiatric treatment.

Digital mental health apps and the therapeutic alliance: initial review.

BACKGROUND: As mental healthcare expands to smartphone apps and other technologies that may offer therapeutic interventions without a therapist involved, it is important to assess the impact of non-traditional therapeutic relationships.AimsTo determine if there were any meaningful data regarding the digital therapeutic alliance in smartphone interventions for serious mental illnesses. METHOD: A literature search was conducted in four databases (PubMed, PsycINFO, Embase and Web of Science). RESULTS: There were five studies that discuss the therapeutic alliance when a mobile application intervention is involved in therapy. However, in none of the studies was the digital therapeutic alliance the primary outcome. The studies looked at different mental health conditions, had different duration of technology use and used different methods for assessing the therapeutic alliance. CONCLUSIONS: Assessing and optimising the digital therapeutic alliance holds the potential to make tools such as smartphone apps more effective and improve adherence to their use. However, the heterogeneous nature of the five studies we identified make it challenging to draw conclusions at this time. A measure is required to evaluate the digital therapeutic alliance.

Understanding the quality, effectiveness and attributes of top-rated smartphone health apps.

OBJECTIVE: This study aimed to understand the attributes of popular apps for mental health and comorbid medical conditions, and how these qualities relate to consumer ratings, app quality and classification by the WHO health app classification framework. METHODS: We selected the 10 apps from the Apple iTunes store and the US Android Google Play store on 20 July 2018 from six disease states: depression, anxiety, schizophrenia, addiction, diabetes and hypertension. Each app was downloaded by two authors who provided information on the apps' attributes, functionality, interventions, popularity, scientific backing and WHO app classification rating. RESULTS: A total of 120 apps were examined. Although none of these apps had Food and Drug Administration marketing approval, nearly 50% made claims that appeared medical. Most apps offered a similar type of services with 87.5% assigned WHO classification 1.4.2 'self-monitoring of health or diagnostic data by a client' or 1.6.1 'client look-up of health information'. The 'last updated' attribute was highly correlated with a quality rating of the app although no apps features (eg, uses Global Positioning System, reminders and so on) were. CONCLUSION: Due to the heterogeneity of the apps, we were unable to define a core set of features that would accurately assess app quality. The number of apps making unsupported claims combined with the number of apps offering questionable content warrants a cautious approach by both patients and clinicians in selecting safe and effective ones. CLINICAL IMPLICATIONS: 'Days since last updated' offers a useful and easy clinical screening test for health apps, regardless of the condition being examined.

Mental Health Mobile Phone App Usage, Concerns, and Benefits Among Psychiatric Outpatients: Comparative Survey Study.

BACKGROUND: Despite the popularity of mental health apps, it is unknown if they are actually used by those with mental illness. This study assessed whether differences in clinic setting may influence the use of mental health apps and which factors influence patient perception of apps. OBJECTIVE: The objective of this study was to gain an understanding of how individuals with mental illness use their mobile phones by exploring their access to mobile phones and their use of mental health apps. METHODS: A single time point survey study was conducted over a 2-week period in February 2018 at two nearby outpatient psychiatry clinics: one serving largely mood and anxiety disorder patients with private insurance staffed by both faculty and residents and the other serving largely psychotic disorder patients in a state Department of Mental Health (DMH) setting. A total of 25 patients at the state DMH clinic also consented for a single time point observation of apps currently installed on their personal mobile phone. RESULTS: A total of 113 patients at the private insurance clinic and 73 at the state DMH clinic completed the survey. Those in the private insurance clinic were more likely to download a mental health app compared to the state DMH clinic, but actual rates of reported current app usage were comparable at each clinic, approximately 10%. Verifying current apps on patients' mobile phones at the state DMH clinic confirmed that approximately 10% had mental health apps installed. Patients at both clinics were most concerned about privacy of mental health apps, although those at the state DMH clinic viewed cost savings as the greatest benefit while those at the private clinic reported time as the greatest benefit. CONCLUSIONS: High interest in mental health apps does not automatically translate into high use. Our results of low but similar rates of mental health app use at diverse clinics suggests DMH patients with largely psychotic disorders are as interested and engaged with apps as those in a private insurance clinic treating largely mood and anxiety disorders. Results from our study also highlight the importance of understanding how actual patients are using apps instead of relying on internet-based samples, which often yield higher results due to their likelihood of being selected.

The Effect of Music on Exercise Intensity among Children with Autism Spectrum Disorder: A Pilot Study.

Children with autism spectrum disorder (ASD) are at risk for obesity, commonly have sleep disorders, and exhibit stereotypic behaviors that disrupt their learning. Vigorous levels of exercise have been shown to ameliorate these issues in children with ASD, but little research exists to provide techniques for motivating children with ASD to engage in exercise. The present study examined the effect of music on exercise intensity in a group of 13 elementary school students with ASD. Data were collected across six days during structured (e.g., verbal and physical prompts) and unstructured (e.g., minimal prompting) exercise periods. During these exercise periods, three music conditions were randomized: no music, slow-tempo music, and fast-tempo music. Exercise intensity, measured in Metabolic Equivalent of Tasks by triaxial accelerometers, was greatest during the structured exercise periods and during the slow music condition. Student characteristics moderated the impact of music condition on exercise intensity, such that students with high levels of adaptive behavior or lower levels of maladaptive behavior displayed greater exercise intensity during the fast music condition.