Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

Exploring User Perspectives on Brief Reflective Questioning Activities for Stress Management: Mixed Methods Study.

BACKGROUND: Current online interventions dedicated to assisting individuals in managing stress and negative emotions often necessitate substantial time commitments. This can be burdensome for users, leading to high dropout rates and reducing the effectiveness of these interventions. This highlights an urgent need for concise digital activities that individuals can swiftly access during instances of negative emotions or stress in their daily lives. OBJECTIVE: The primary aim of this study was to investigate the viability of using a brief digital exercise, specifically a reflective questioning activity (RQA), to help people reflect on their thoughts and emotions about a troubling situation. The RQA is designed to be quick, applicable to the general public, and scalable without requiring a significant support structure. METHODS: We conducted 3 simultaneous studies. In the first study, we recruited 48 participants who completed the RQA and provided qualitative feedback on its design through surveys and semistructured interviews. In the second study, which involved 215 participants from Amazon Mechanical Turk, we used a between-participants design to compare the RQA with a single-question activity. Our hypotheses posited that the RQA would yield greater immediate stress relief and higher perceived utility, while not significantly altering the perception of time commitment. To assess these, we measured survey completion times and gathered multiple self-reported scores. In the third study, we assessed the RQA's real-world impact as a periodic intervention, exploring engagement via platforms such as email and SMS text messaging, complemented by follow-up interviews with participants. RESULTS: In our first study, participants appreciated the RQA for facilitating structured reflection, enabling expression through writing, and promoting problem-solving. However, some of the participants experienced confusion and frustration, particularly when they were unable to find solutions or alternative perspectives on their thoughts. In the second study, the RQA condition resulted in significantly higher ratings (P=.003) for the utility of the activity and a statistically significant decrease (P<.001) in perceived stress rating compared with the single-question activity. Although the RQA required significantly more time to be completed (P<.001), there was no statistically significant difference in participants' subjective perceived time commitment (P=.37). Deploying the RQA over 2 weeks in the third study identified some potential challenges to consider for such activities, such as the monotony of doing the same activity several times, the limited affordances of mobile phones, and the importance of having the prompts align with the occurrence of new troubling situations. CONCLUSIONS: This paper describes the design and evaluation of a brief online self-reflection activity based on cognitive behavioral therapy principles. Our findings can inform practitioners and researchers in the design and exploration of formats for brief interventions to help people with everyday struggles.

Small Steps over time: A longitudinal usability test of an automated interactive text messaging intervention to support self-management of depression and anxiety symptoms.

BACKGROUND: Digital mental health interventions (DMHIs) offer potential solutions for addressing mental health care gaps, but often suffer from low engagement. Text messaging is one promising medium for increasing access and sustaining user engagement with DMHIs. This paper examines the Small Steps SMS program, an 8-week, automated, adaptive text message-based intervention for depression and anxiety. METHODS: We conducted an 8-week longitudinal usability test of the Small Steps SMS program, recruiting 20 participants who met criteria for major depressive disorder and/or generalized anxiety disorder. Participants used the automated intervention for 8 weeks and completed symptom severity and usability self-report surveys after 4 and 8 weeks of intervention use. Participants also completed individual interviews to provide feedback on the intervention. RESULTS: Participants responded to automated messages on 70 % of study days and with 85 % of participants sending responses to messages in the 8th week of use. Usability surpassed established cutoffs for software that is considered acceptable. Depression symptom severity decreased significantly over the usability test, but reductions in anxiety symptoms were not significant. Participants noted key areas for improvement including addressing message volume, aligning message scheduling to individuals' availability, and increasing the customizability of content. LIMITATIONS: This study does not contain a control group. CONCLUSIONS: An 8-week automated interactive text messaging intervention, Small Steps SMS, demonstrates promise with regard to being a feasible, usable, and engaging method to deliver daily mental health support to individuals with symptoms of anxiety and depression.

A text messaging intervention to support the mental health of young adults: User engagement and feedback from a field trial of an intervention prototype.

Background: Young adults have high rates of mental health conditions, but most do not want or cannot access treatment. By leveraging a medium that young adults routinely use, text messaging programs have potential to keep young adults engaged with content supporting self-management of mental health issues and can be delivered inexpensively at scale. We designed an intervention that imparts strategies for self-managing mental health symptoms through interactive text messaging dialogues and engages users through novelty and variety in strategies (from cognitive behavioral therapy, acceptance and commitment therapy, and positive psychology) and styles of interaction (e.g., prompts, peer stories, writing tasks). Methods: The aim of this mixed-methods study was to pilot 1- and 2-week versions of an interactive text messaging intervention among young adults (ages 18-25), and to obtain feedback to guide intervention refinements. Young adults were recruited via a mental health advocacy website and snowball sampling at a North American University. We used Wizard-of-Oz methods in which study staff sent messages based on a detailed script. Transcripts of interviews were subject to qualitative analysis to identify aspects of the program that need improvements, and to gather participant perspectives on possible solutions. Results: Forty-eight individuals ages 18-25 participated in the study (mean age: 22.0). 85 % responded to the program at least once. Among those who ever responded, they replied to messages on 85 % of days, and with engagement sustained over the study period. Participants endorsed the convenience of text messaging, the types of interactive dialogues, and the variety of content. They also identified needed improvements to message volume, scheduling, and content. Conclusions: Young adults showed high levels of engagement and satisfaction with a texting program supporting mental health self-management. The program may be improved through refining personalization, timing, and message volume, and extending content to support use over a longer timeframe. If shown to be effective in randomized trials, this program has potential to help address a substantial treatment gap in young adults' mental health.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing.

Without a nuanced understanding of users' perspectives and contexts, text messaging tools for supporting psychological wellbeing risk delivering interventions that are mismatched to users' dynamic needs. We investigated the contextual factors that influence young adults' day-to-day experiences when interacting with such tools. Through interviews and focus group discussions with 36 participants, we identified that people's daily schedules and affective states were dominant factors that shape their messaging preferences. We developed two messaging dialogues centered around these factors, which we deployed to 42 participants to test and extend our initial understanding of users' needs. Across both studies, participants provided diverse opinions of how they could be best supported by messages, particularly around when to engage users in more passive versus active ways. They also proposed ways of adjusting message length and content during periods of low mood. Our findings provide design implications and opportunities for context-aware mental health management systems.

System design of a text messaging program to support the mental health needs of non-treatment seeking young adults.

Young adults (ages 18-25) experience the highest levels of mental health problems of any adult age group, but have the lowest mental health treatment rates. Text messages are the most used feature on the mobile phone and provide an opportunity to reach non-treatment engaged users throughout the day in a conversational manner. We present the design of an automated text message-based intervention for symptom self-management. The intervention comprises: (1) psychological strategies (i.e., types of evidence-based techniques leveraged to achieve symptom reduction) and (2) interaction types or the form that intervention content takes as it is delivered to and elicited from users.

Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults.

Young adults have high rates of mental health conditions, but most do not want or cannot access formal treatment. We therefore recruited young adults with depression or anxiety symptoms to co-design a digital tool for self-managing their mental health concerns. Through study activities-consisting of an online discussion group and a series of design workshops-participants highlighted the importance of easy-to-use digital tools that allow them to exercise independence in their self-management. They described ways that an automated messaging tool might benefit them by: facilitating experimentation with diverse concepts and experiences; allowing variable depth of engagement based on preferences, availability, and mood; and collecting feedback to personalize the tool. While participants wanted to feel supported by an automated tool, they cautioned against incorporating an overtly human-like motivational tone. We discuss ways to apply these findings to improve the design and dissemination of digital mental health tools for young adults.

"I Kind of Bounce off It": Translating Mental Health Principles into Real Life Through Story-Based Text Messages.

Adopting new psychological strategies to improve mental wellness can be challenging since people are often unable to anticipate how new habits are applicable to their circumstances. Narrative-based interventions have the potential to alleviate this burden by illustrating psychological principles in an applied context. In this work, we explore how stories can be delivered via the ubiquitous and scalable medium of text messaging. Through formative work consisting of interviews and focus group discussions with 15 participants, we identified desirable elements of stories about mental health, including authenticity and relatability. We then deployed story-based text messages to 42 participants to explore challenges regarding both the stories' content (e.g., specific versus generalized) and format (e.g., story length). We observed that our stories helped participants reflect on and identify flaws in their thinking patterns. Our findings highlight design implications and opportunities for mental wellness interventions that utilize stories in text messaging services.

Understanding the Social Determinants of Mental Health of Undergraduate Students in Bangladesh: Interview Study.

BACKGROUND: The undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. OBJECTIVE: This study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia; study the manifestation of these determinants in their day-to-day lives; and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. METHODS: We conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. RESULTS: Our results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). CONCLUSIONS: Although some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world.

Ellagic acid restored lead-induced nephrotoxicity by anti-inflammatory, anti-apoptotic and free radical scavenging activities.

INTRODUCTION: long-term environmental and occupational exposure to lead, which is a ubiquitous industrial pollutant, causes significant damage to tissues of kidney. This report aims to address this debilitating issue. A natural polyphenolic compound, Ellagic acid (EA) is having numerous potential medicinal properties. In this present study nephroprotective effects of EA has been evaluated in a rodent model with lead-induced toxicity. METHODS: Rats were treated with EA doses of 50 mg/kg and 25 mg/kg and simultaneously co-administered with lead acetate (60 mg/kg) for 2 months through oral route. The extent to which EA treatment provides nephroprotective effect was estimated by measurement of serum biomarkers, tissue antioxidants, inflammatory mediators, apoptosis, autophagy pathway and histological examination. RESULTS: EA treatment caused significant restoration in the level of serum biomarkers, tissue antioxidants and histological architecture of renal tissue. Treatment with either of the doses of EA causes restoration of pro-inflammatory mediators to approximately pre-exposure concentration. This phenomena is caused by suppression of expression levels of inflammatory molecules like tumour necrosis factor-α (TNF-α), nuclear factor kappa B (NF-κB), interleukin-6 (IL-6), and interleukin-1ß (IL-1ß), as well as functional expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Moreover, it was also observed that EA suppressed apoptotic and autophagic pathway by reduction of expression of light chain 3B (LC3B) level which are the oxidative DNA damage markers of renal tissue. CONCLUSION: It can be safely concluded that EA provides protection against lead-induced nephrotoxicity to a significant degree.

Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices.

BACKGROUND: With the rapid growth rate of newly sequenced genomes, species tree inference from genes sampled throughout the whole genome has become a basic task in comparative and evolutionary biology. However, substantial challenges remain in leveraging these large scale molecular data. One of the foremost challenges is to develop efficient methods that can handle missing data. Popular distance-based methods, such as NJ (neighbor joining) and UPGMA (unweighted pair group method with arithmetic mean) require complete distance matrices without any missing data. RESULTS: We introduce two highly accurate machine learning based distance imputation techniques. These methods are based on matrix factorization and autoencoder based deep learning architectures. We evaluated these two methods on a collection of simulated and biological datasets. Experimental results suggest that our proposed methods match or improve upon the best alternate distance imputation techniques. Moreover, these methods are scalable to large datasets with hundreds of taxa, and can handle a substantial amount of missing data. CONCLUSIONS: This study shows, for the first time, the power and feasibility of applying deep learning techniques for imputing distance matrices. Thus, this study advances the state-of-the-art in phylogenetic tree construction in the presence of missing data. The proposed methods are available in open source form at https://github.com/Ananya-Bhattacharjee/ImputeDistances .

Cardioprotective effect of curcumin and piperine combination against cyclophosphamide-induced cardiotoxicity.

OBJECTIVE: Curcumin is a well-established cardioprotective phytoconstituent, but the poor bioavailability associated with it is always a matter of therapeutic challenge. The present study was undertaken to increase the therapeutic efficacy of curcumin by combining with bio-enhancer like piperine against cyclophosphamide (CP)-induced cardiotoxicity in rats. MATERIALS AND METHODS: Rats (n = 8) were treated with curcumin (200 mg/kg, p.o.) alone and different dose combination of curcumin (100, 50, 25 mg/kg, p.o.) and piperine (20 mg/kg, p.o.) for 10 days. All the treated groups were subjected to CP (200 mg/kg, i.p.) toxicity on day 1. Twenty-four hours after the last treatment, the effects were evaluated by changes in electrocardiographic (ECG) parameters, serum biomarkers, lipid profile, tissue antioxidants, and histopathological examination. Serum and tissue homogenate parameters were measured by semi-autoanalyzer and spectrophotometer, respectively. Results obtained were assessed by one-way analysis of variance followed by Tukey-Karmer multiple comparison test. RESULTS: Incorporation of piperine with the doses of 50 and 25 mg/kg with curcumin exhibited significant beneficial effect compared to curcumin alone-treated group. The best effective group was a combination of curcumin 50 mg/kg with piperine 20 mg/kg which showed extremely significant (P < 0.001) decrease and increase in ECG and serum biomarker level, respectively, and moderate significant (P < 0.01) decrease in lipid profile, antioxidant levels, and histopathological score, compared to curcumin alone-treated group. CONCLUSION: From this study, it can be concluded that a novel dose combination of curcumin (50 mg/kg) with piperine (20 mg/kg) exhibited profound cardioprotection compared to curcumin (200 mg/kg) alone-treated group.

The potential for interaction of tolbutamide with pomegranate juice against diabetic induced complications in rats.

BACKGROUND: Pomegranate can inhibit cytochrome P450 (CYP) 2C9 activity, which is largely responsible for the metabolism of sulfonylureas. The present study was undertaken to evaluate the pharmacokinetic and pharmacodynamic interaction of pomegranate and tolbutamide (TOL) against diabetic-induced complications. METHODS: Diabetes was induced via administration of alloxan (150 mg/kg, intraperitoneally). Rats (n = 8) were treated with pomegranate juice (PJ) [3 mL/animal, per os (p.o.)], TOL (20 mg/kg, p.o.), and their combination for 4 weeks. Twenty-four hours after the last treatment, the pharmacodynamic interaction of PJ and TOL was evaluated by antinociceptive activity, electrocardiographic parameters, serum glucose, biomarkers, and lipid profile values. The influence of PJ on the pharmacokinetics of TOL was studied using the high performance liquid chromatography method. RESULTS: The combination of PJ and TOL resulted in a significant improvement against diabetic complications compared to the group treated with TOL alone. The combination group was found to be the best protective group by significant improvement of antinociceptive activity, restoration of electrocardiographic parameters, serum glucose, biomarkers, and lipid profile compared to the group treated with TOL alone. Results of the pharmacokinetic study revealed that PJ increases bioavailability and half-life, along with a decrease in clearance and elimination rate of TOL. CONCLUSION: From this study, it can be concluded that the combination of PJ and TOL exhibited profound protection compared to TOL alone against diabetic complications. The findings of pharmacokinetic interaction justified the results of pharmacodynamic interaction.

Potential interaction of green tea extract with hydrochlorothiazide against doxorubicin-induced myocardial damage.

BACKGROUND: Treatment of ischemic hypertensive patients with hydrochlorothiazide can precipitate cardiac arrhythmias. Green tea, by virtue of its antioxidant potential, is responsible for cardio-protective activity. OBJECTIVE: The present study was under taken to evaluate the pharmacodynamic interaction of green tea extract with hydrochlorothiazide (HCTZ) against doxorubicin (DOX)-induced myocardial toxicity. MATERIALS AND METHODS: Rats were treated with high (500 mg/kg, p.o.) and low (100 mg/kg, p.o.) dose of green tea extract in alone and interactive groups for 28 days. Standard, high and low dose of interactive groups received hydrochlorothiazide (10 mg/kg, p.o.) for the last 7 days. Apart from normal controls, all other groups were subjected to DOX (3 mg/kg, i.p.) toxicity on Days 1, 7, 14, 21 and 28, and the effect of different treatments was evaluated by changes in electrocardiographic parameters, serum biomarkers and tissue antioxidant levels. Apart from that, lipid profile and histological studies were also carried out. RESULTS: Compared with the DOX control group, both high and low dose of green tea exhibited a significant decrease in serum biomarkers and increase in tissue antioxidant levels. Green tea treatment was also responsible for significant improvement in ECG parameter, lipid profile and histological score. Incorporation of high and low dose of green tea with HCTZ exhibited significant protection compared with the HCTZ alone treated group. CONCLUSION: The present findings clearly suggest that the green tea extract dose-dependently reduces DOX-induced myocardial toxicity. Green tea when combined with HCTZ can reduce the associated side-effects and exhibits myocardial protection.

Pharmacodynamic Interaction of Green Tea Extract with Hydrochlorothiazide against Cyclophosphamide-Induced Myocardial Damage.

OBJECTIVE: Treatment of ischemic hypertensive patients with hydrochlorothiazide can precipitate cardiac arrhythmias. Green tea by virtue of its antioxidant potential is responsible for cardio-protective activity. The present study was undertaken to evaluate the pharmacodynamic interaction of green tea extract with hydrochlorothiazide against cyclophosphamide-induced myocardial toxicity. MATERIALS AND METHODS: Rats were treated with high (500 mg/kg, p.o.) and low (100 mg/kg, p.o.) dose of green tea extract in alone and interactive groups for 10 days. Standard, high, and low dose of interactive groups received hydrochlorothiazide (10 mg/kg, p.o.) for last 7 days. Apart from normal control, all other groups were subjected to cyclophosphamide (200 mg/kg, i.p.) toxicity on day first and the effects of different treatments were evaluated by changes in electrocardiographic parameters, serum biomarkers, and tissue antioxidant levels. Apart from that, lipid profile and histological studies were also carried out. RESULTS: Compared to cyclophosphamide control group, both high and low dose of green tea exhibited significant decrease in serum biomarkers and increase in tissue antioxidant levels. Green tea treatment was also responsible for significant improvement in echocardiography (ECG) parameter, lipid profile, and histological score. Incorporation of high and low dose of green tea with hydrochlorothiazide-exhibited significant protection compared to hydrochlorothiazide-alone-treated group. CONCLUSION: The present findings clearly suggested that green tea extract dose dependently reduces cyclophosphamide-induced myocardial toxicity. Green tea when combined with hydrochlorothiazide can reduce the associated side effects and exhibits myocardial protection.

Potential Interaction of Green Tea Extract with Hydrochlorothiazide on Diuretic Activity in Rats.

Treatment of ischemic hypertensive patients with hydrochlorothiazide can precipitate cardiac arrhythmias. The present study was undertaken to evaluate the diuretic potential of green tea alone and its effects on hydrochlorothiazide in interactive groups. Rats were treated with high (500 mg/kg, p.o.) and low (100 mg/kg, p.o.) dose of green tea extract in alone and interactive groups for 30 days. Standard, high, and low dose interactive groups received hydrochlorothiazide (10 mg/kg, p.o.) on the day of experiment. Effect of different treatments was that assessed by evaluating diuretic action, diuretic activity, percentage of saline load excreted, and sodium and potassium levels in urine. Green tea in both high and low doses showed significant diuretic potential and when it is combined with hydrochlorothiazide resulted in significant improvement in the activity compared to hydrochlorothiazide alone treated group. It can be concluded that green tea extract when combined with hydrochlorothiazide showed significant increase in diuretic activity. Most important observation of the present study is even though the combination increases the diuretic potential, it is responsible for decrease in urinary potassium loss.