A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning

Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study

BACKGROUND

Perinatal depression affects a significant number of women during pregnancy and after birth, and early identification is imperative for timely interventions and improved prognosis. Mobile apps offer the potential to overcome barriers to health care provision and facilitate clinical research. However, little is known about users' perceptions and acceptability of these apps, particularly digital phenotyping and ecological momentary assessment apps, a relatively novel category of apps and approach to data collection. Understanding user's concerns and the challenges they experience using the app will facilitate adoption and continued engagement.

OBJECTIVE

This qualitative study explores the experiences and attitudes of users of the Mom2B mobile health (mHealth) research app (Uppsala University) during the perinatal period. In particular, we aimed to determine the acceptability of the app and any concerns about providing data through a mobile app.

METHODS

Semistructured focus group interviews were conducted digitally in Swedish with 13 groups and a total of 41 participants. Participants had been active users of the Mom2B app for at least 6 weeks and included pregnant and postpartum women, both with and without depression symptomatology apparent in their last screening test. Interviews were recorded, transcribed verbatim, translated to English, and evaluated using inductive thematic analysis.

RESULTS

Four themes were elicited: acceptability of sharing data, motivators and incentives, barriers to task completion, and user experience. Participants also gave suggestions for the improvement of features and user experience.

CONCLUSIONS

The study findings suggest that app-based digital phenotyping is a feasible and acceptable method of conducting research and health care delivery among perinatal women. The Mom2B app was perceived as an efficient and practical tool that facilitates engagement in research as well as allows users to monitor their well-being and receive general and personalized information related to the perinatal period. However, this study also highlights the importance of trustworthiness, accessibility, and prompt technical issue resolution in the development of future research apps in cooperation with end users. The study contributes to the growing body of literature on the usability and acceptability of mobile apps for research and ecological momentary assessment and underscores the need for continued research in this area.

EHealth technologies in parental care for preterm infants: an integrative review

The eHealth technologies promote parental care practices for preterm infants. Nonetheless, we should underscore the abundant information and available apps and disparities in these resources' quality, usability, and reliability. This article examines eHealth technologies directed at parents to care for preterm infants. An integrative review was conducted across the principal health databases (Capes, EBSCO, BVS, PubMed, Scholar, and SciELO), selecting works published from 2011 to 2022 in Portuguese and English, focusing on the use of eHealth technologies for the care of preterm infants. We identified 13 articles related to information and communication technologies in strategies for educating and promoting the health of preterm infants and their parents and the importance of evaluating and validating eHealth technologies in maternal and child health promotion. Properly validated eHealth technologies can be crucial in supporting parents in promoting health and providing care for preterm infants after hospital discharge, which, in turn, can drive the evolution of healthcare systems and improve clinical practices.

Digital screening for mental health in pregnancy and postpartum: A systematic review

PURPOSE

This systematic review aimed to determine if digital screening for mental health in pregnancy and postpartum is acceptable, feasible and more effective than standard care (paper-and pen-based screening or no screening). The second aim was to identify barriers and enablers to implementing digital screening in pregnancy and postpartum.

METHOD

OVID MEDLINE, PsycINFO, SCOPUS, CINAHL, Embase, Web of Science, Joanna Briggs Database and All EMB reviews incorporating Cochrane Database of Systematic Reviews (OVID) were systematically searched for articles that evaluated digital screening for mental health in pregnancy and postpartum between 2000 and 2021. Qualitative articles were deductively mapped to the Theoretical Domains Framework (TDF).

RESULTS

A total of 34 articles were included in the analysis, including qualitative, quantitative and mixed-methods studies. Digital screening was deemed acceptable, feasible and effective. TDF domains for common barriers included environmental context and resources, skills, social/professional role and identity and beliefs about consequences. TDF domains for common enablers included knowledge, social influences, emotion and behavioural regulation.

CONCLUSION

When planning to implement digital screening, consideration should be made to have adequate training, education and manageable workload for healthcare professionals (HCP's). Organisational resources and support are important, as well as the choice of the appropriate digital screening assessment and application setting for women. Theory-informed recommendations are provided for both healthcare professionals and women to inform future clinical practice.

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Introduction

Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences.

Objective

Our objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children's National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors.

Study design

Our study design optimized eight ML algorithms - Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network - to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score.

Results

The results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model's performance is comparable to other common ML models.

Conclusion

Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

Predictive analytics and early intervention in healthcare social work: a scoping review

This scoping review investigates the untapped potential of predictive analytics in healthcare social work, specifically targeting early intervention frameworks. Despite the escalating attention predictive analytics has garnered across multiple disciplines, its tailored application in social work remains notably sparse. This study endeavors to fill this lacuna by meticulously reviewing the extant literature and delineating the prospective advantages and inherent constraints of integrating predictive analytics into healthcare social work. The outcomes of this inquiry enrich the prevailing dialogue on the utility of predictive analytics in healthcare, offering indispensable perspectives for practitioners and policymakers in the social work domain.

Predicting prenatal depression and assessing model bias using machine learning models

Perinatal depression (PND) is one of the most common medical complications during pregnancy and postpartum period, affecting 10-20% of pregnant individuals. Black and Latina women have higher rates of PND, yet they are less likely to be diagnosed and receive treatment. Machine learning (ML) models based on Electronic Medical Records (EMRs) have been effective in predicting postpartum depression in middle-class White women but have rarely included sufficient proportions of racial and ethnic minorities, which contributed to biases in ML models for minority women. Our goal is to determine whether ML models could serve to predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. We extracted EMRs from a hospital in a large urban city that mostly served low-income Black and Hispanic women (N=5,875) in the U.S. Depressive symptom severity was assessed from a self-reported questionnaire, PHQ-9. We investigated multiple ML classifiers, used Shapley Additive Explanations (SHAP) for model interpretation, and determined model prediction bias with two metrics, Disparate Impact, and Equal Opportunity Difference. While ML model (Elastic Net) performance was low (ROCAUC=0.67), we identified well-known factors associated with PND, such as unplanned pregnancy and being single, as well as underexplored factors, such as self-report pain levels, lower levels of prenatal vitamin supplement intake, asthma, carrying a male fetus, and lower platelet levels blood. Our findings showed that despite being based on a sample mostly composed of 75% low-income minority women (54% Black and 27% Latina), the model performance was lower for these communities. In conclusion, ML models based on EMRs could moderately predict depression in early pregnancy, but their performance is biased against low-income minority women.

Predictive models for predicting the risk of maternal postpartum depression: A systematic review and evaluation

OBJECTIVES

Clinical prediction models have been widely used to screen and diagnose postpartum depression (PPD). This study systematically reviews and evaluates the risk of bias and the applicability of PPD prediction models.

METHODS

A systematic search was performed in eight databases from inception to June 1, 2022. The literature was independently screened, and data were extracted by two investigators using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS). The risk of bias and applicability was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

After the screening, 12 studies of PPD risk prediction models were included, with the area under the ROC curve of the models ranging from 0.611 to 0.937. The most-reported predictors of PPD included several aspects, including prenatal mood disorders, endocrine and hormonal influences, psychosocial aspects, the influence of family factors, and somatic illness factors. The applicability of all studies was good. However, there was some bias, mainly due to inadequate outcome events, missing data not appropriately handled, lack of model performance assessment, and overfitting of the models.

CONCLUSIONS

This systematic review and evaluation indicate that most present PPD prediction models have a high risk of bias during development and validation. Despite some models' predictive solid performance, the models' clinical practice rate is low. Therefore, future research should develop predictive models with excellent performance in all aspects and clinical applicability to better inform maternal medical decisions.

Machine learning-based clinical decision support systems for pregnancy care: A systematic review

BACKGROUND

Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care.

OBJECTIVE

This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers.

METHODS

We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis.

RESULTS

17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing.

CONCLUSION

Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.

An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches

BACKGROUND

Postpartum depression (PPD) is one of the most common psychiatric disorders for women after delivery. The establishment of an effective PPD prediction model helps to distinguish high-risk groups, and verifying whether such high-risk groups can benefit from drug intervention is very important for clinical guidance.

METHODS

We collected data of parturients that underwent a cesarean delivery. The Control group was divided into a training cohort and a testing cohort. Six different ML models were constructed and we compared their prediction performance in the testing cohort. For model interpretation, we introduced SHapley Additive exPlanations (SHAP). Then, training cohort, ketamine group and dexmedetomidine (DEX) group were classified as high or low risk for PPD by the model. A 1:1 propensity score matching (PSM) was performed to compare the incidence of PPD between two groups in different risk cohorts.

RESULTS

Extreme gradient enhancement (XGB) had the best recognition effect, with an area under the receiver operating characteristic curve (AUROC) of 0.789 (95 % CI 0.742-0.836) in the training cohort and 0.744 (95 % CI 0.655-0.823) in the testing cohort, respectively. A threshold of 21.5 % PPD risk probability was determined. After PSM, the results showed that the incidence of PPD in the two intervention groups was significantly different from the control group in the high-risk cohort (P < 0.001) but not in the low-risk cohort (P > 0.001).

CONCLUSION

Our study demonstrated that the XGB algorithm provided a more accurate in prediction of PPD risk, and it was beneficial to receive early intervention for the high-risk groups distinguished by the model.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

Application of machine learning in predicting the risk of postpartum depression: A systematic review

BACKGROUND

Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk.

METHODS

We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk.

LIMITATIONS

Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis.

CONCLUSIONS

Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.

Consumer acceptance of using a digital technology to manage postpartum depression

The goal of the study was to evaluate the end user experience using the MamaLift Plus app for 2 weeks to support the treatment of their postpartum depression (PPD). A total of 14 participants completed the study and their experiences are reported in this publication. Participants reported that MamaLift Plus is an acceptable, highly usable, and practical mobile tool to use weekly for the management of their PPD. More research is warranted to evaluate the benefit of digital behavior health interventions, especially in patient populations where mental health care may be limited or harder to access by patients.

Machine learning in the prediction of postpartum depression: A review

BACKGROUND

Current screening options in the setting of postpartum depression (PPD) are firmly rooted in self-report symptom-based tools. The implementation of the modern machine learning (ML) approaches might, in this context, represent a way to refine patient screening by precisely identifying possible PPD predictors and, subsequently, a population at risk of developing the disease, in an effort to lower its morbidity, mortality and its economic burden.

METHODS

We performed a bibliographic search on PubMed and Embase looking for studies aimed at the identification of PPD predictors using ML techniques.

RESULTS

Among the 482 articles retrieved, 11 met the inclusion criteria. The most used algorithm was the support vector machine. Notably, all studies reached an area under the curve above 0.7, ultimately suggesting that the prediction of PPD could be feasible. Variables obtained from sociodemographic and clinical aspects (psychiatric and gynecological factors) seem to be the most reliable. Only three studies employed biological variables, in the form of blood, genetic and epigenetic predictors, while no study employed imaging techniques.

LIMITATIONS

The literature on PPD prediction via ML techniques is currently scarce, with most studies employing different variables selection and ML algorithms, ultimately reducing the generalizability of the results.

CONCLUSIONS

The identification of a population at risk of developing PPD might be feasible with current technology and clinical knowledge. Further studies are necessary to clarify how such an approach could be implemented into clinical practice.

Functional and Technical Aspects of Self-management mHealth Apps: Systematic App Search and Literature Review

BACKGROUND

Although the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective.

OBJECTIVE

This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps.

METHODS

This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub.

RESULTS

In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges.

CONCLUSIONS

Self-management mHealth apps may offer an essential means of managing users' health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.

Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol

INTRODUCTION

Perinatal complications, such as perinatal depression and preterm birth, are major causes of morbidity and mortality for the mother and the child. Prediction of high risk can allow for early delivery of existing interventions for prevention. This ongoing study aims to use digital phenotyping data from the Mom2B smartphone application to develop models to predict women at high risk for mental and somatic complications.

METHODS AND ANALYSIS

All Swedish-speaking women over 18 years, who are either pregnant or within 3 months postpartum are eligible to participate by downloading the Mom2B smartphone app. We aim to recruit at least 5000 participants with completed outcome measures. Throughout the pregnancy and within the first year postpartum, both active and passive data are collected via the app in an effort to establish a participant's digital phenotype. Active data collection consists of surveys related to participant background information, mental and physical health, lifestyle, and social circumstances, as well as voice recordings. Participants' general smartphone activity, geographical movement patterns, social media activity and cognitive patterns can be estimated through passive data collection from smartphone sensors and activity logs. The outcomes will be measured using surveys, such as the Edinburgh Postnatal Depression Scale, and through linkage to national registers, from where information on registered clinical diagnoses and received care, including prescribed medication, can be obtained. Advanced machine learning and deep learning techniques will be applied to these multimodal data in order to develop accurate algorithms for the prediction of perinatal depression and preterm birth. In this way, earlier intervention may be possible.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the Swedish Ethical Review Authority (dnr: 2019/01170, with amendments), and the project fully fulfils the General Data Protection Regulation (GDPR) requirements. All participants provide consent to participate and can withdraw their participation at any time. Results from this project will be disseminated in international peer-reviewed journals and presented in relevant conferences.

Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study

Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.

Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms

Background:

Despite the importance of early identification and treatment, postpartum depression often remains largely undiagnosed with unreported symptoms. While research has identified several factors as prompting help-seeking for postpartum depression symptoms, no research has examined help-seeking for postpartum depression using data from a multi-state/jurisdictional survey analyzed with machine learning techniques.

Objectives:

This study examines help-seeking among people with postpartum depression symptoms using and demonstrating the utility of machine learning techniques.

Methods:

Data from the 2016–2018 Pregnancy Risk Assessment Monitoring System, a cross-sectional survey matched with birth certificate data, were used. Six US states/jurisdictions included the outcome help-seeking for postpartum depression symptoms and were used in the analysis. An ensemble method, “Super Learner,” was used to identify the best combination of algorithms and most important variables that predict help-seeking among 1920 recently pregnant people who screen positive for postpartum depression symptoms.

Results:

The Super Learner predicted well and had an area under the receiver operating curve of 87.95%. It outperformed the highest weighted algorithms which were conditional random forest and stochastic gradient boosting. The following variables were consistently among the top 10 most important variables across the algorithms for predicting increased help-seeking: participants who reported having been diagnosed with postpartum depression, having depression during pregnancy, living in particular US states, being a White compared to Black or Asian American individual, and having a higher maternal body mass index at the time of the survey.

Conclusion:

These results show the utility of using ensemble machine learning techniques to examine complex topics like help-seeking. Healthcare providers should consider the factors identified in this study when screening and conducting outreach and follow-up for postpartum depression symptoms.

Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018

Depression is one of the most common mental health problems in middle-aged and elderly people. The establishment of risk factor-based depression risk assessment model is conducive to early detection and early treatment of high-risk groups of depression. Five machine learning models (logistic regression (LR); back propagation (BP); random forest (RF); support vector machines (SVM); category boosting (CatBoost) were used to evaluate the depression among 8374 middle-aged people and 4636 elderly people in the NHANES database from 2011 to 2018. In the 2011-2018 cycle, the estimated prevalence of depression was 8.97% in the middle-aged participants and 8.02% in the elderly participants. Among the middle-aged and elderly participants, CatBoost was the best model to identify depression, and its area under the working characteristic curve (AUC) reaches the highest. The second is LR model and SVM model, while the performance of BP and RF model was slightly worse. The primary influencing factor of depression in middle-aged male is alanine aminotransferase. All five machine learning models can identify the occurrence of depression in the NHANES data set through social demographics, lifestyle, laboratory data and other data of middle-aged and elderly people, and among five models, the CatBoost model performed best.

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

BACKGROUND

Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years.

OBJECTIVE

This study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD).

METHODS

We used a scoping review methodology using the Arksey and O'Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles' ML model, data type, and study results.

RESULTS

A total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93).

CONCLUSIONS

ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence.

Predicting women with depressive symptoms postpartum with machine learning methods

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers’ and children’s health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.

The Development of the Postpartum Depression Self-Management Mobile Application "Happy Mother"

Postpartum depression is the most common mood disorder that occurs after childbirth, rendering it a significant public health problem. Information and communication technologies hold tremendous promise for expanding the reach of quality mental healthcare and closing the treatment gap for depression. In particular, given that mobile applications are inexpensive and provide information systematically, they are suitable as a method of health management that does not require visiting a medical center. The purposes of this study were to document the process of developing a mobile application for the self-management of postpartum depression and to share usability test results. The mobile application "Happy Mother" was developed based on the first five of seven stages in the mobile application development lifecycle model. Components of cognitive behavioral therapy were adopted to guide content development for "Happy Mother." The usability of the completed mobile application was tested in the following three steps: it increased awareness of mood, promoted self-management, and implemented specific methods a mother can use in her daily life to improve mood, including modifications made based on the results of the usability test.

Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study

BACKGROUND

Currently, postpartum depression (PPD) screening is mainly based on self-report symptom-based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent of PPD. We developed and validated a machine learning-based PPD prediction model utilizing electronic health record (EHR) data, and identified novel PPD predictors.

METHODS

A nationwide longitudinal cohort that included 214,359 births between January 2008 and December 2015, divided into model training and validation sets, was constructed utilizing Israel largest health maintenance organization's EHR-database. PPD was defined as new diagnosis of a depressive episode or antidepressant prescription within the first year postpartum. A gradient-boosted decision tree algorithm was applied to EHR-derived sociodemographic, clinical, and obstetric features.

RESULTS

Among the birth cohort, 1.9% (n = 4104) met the case definition of new-onset PPD. In the validation set, the prediction model achieved an area under the curve (AUC) of 0.712 (95% confidence interval, 0.690-0.733), with a sensitivity of 0.349 and a specificity of 0.905 at the 90th percentile risk threshold, identifying PPDs at a rate more than three times higher than the overall set (positive and negative predictive values were 0.074 and 0.985, respectively). The model's strongest predictors included both well-recognized (e.g., past depression) and less-recognized (differing patterns of blood tests) PPD risk factors.

CONCLUSIONS

Machine learning-based models incorporating EHR-derived predictors, could augment symptom-based screening practice by identifying the high-risk population at greatest need for preventive intervention, before development of PPD.

Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines

Perinatal depression and anxiety are defined to be the mental health problems a woman faces during pregnancy, around childbirth, and after child delivery. While this often occurs in women and affects all family members including the infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal depression and anxiety worldwide, especially in low-income countries, are extremely high. The wide majority suffers from mild to moderate depression with the risk of leading to impaired child–mother relationship and infant health, few women end up taking their own lives. Owing to high costs and non-availability of resources, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on mother and child’s health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the trained model. Multilayer perceptron and support vector classifier achieved an area under the receiving operating characteristic curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety, respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital’s gynecology and obstetrics departments.

Early identification of postpartum depression using demographic, clinical, and digital phenotyping

Postpartum depression (PPD) and adjustment disorder (AD) affect up to 25% of women after childbirth. However, there are no accurate screening tools for either disorder to identify at-risk mothers and enable them to benefit from early intervention. Combinations of anamnestic, clinical, and remote assessments were evaluated for an early and accurate identification of PPD and AD. Two cohorts of mothers giving birth were included in the study (N = 308 and N = 193). At baseline, participants underwent a detailed sociodemographic-anamnestic and clinical interview. Remote assessments were collected over 12 weeks comprising mood and stress levels as well as depression and attachment scores. At 12 weeks postpartum, an experienced clinician assigned the participants to three distinct groups: women with PPD, women with AD, and healthy controls (HC). Combinations of these assessments were assessed for an early an accurate detection of PPD and AD in the first cohort and, after pre-registration, validated in a prospective second cohort. Combinations of postnatal depression, attachment (for AD) and mood scores at week 3 achieved balanced accuracies of 93 and 79% for differentiation of PPD and AD from HC in the validation cohort and balanced accuracies of 87 and 91% in the first cohort. Differentiation between AD and PPD, with a balanced accuracy of 73% was possible at week 6 based on mood levels only with a balanced accuracy of 73% in the validation cohort and a balanced accuracy of 76% in the first cohort. Combinations of in clinic and remote self-assessments allow for early and accurate detection of PPD and AD as early as three weeks postpartum, enabling early intervention to the benefit of both mothers and children.

Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women

OBJECTIVE

There is a scarcity in tools to predict postpartum depression (PPD). We propose a machine learning framework for PPD risk prediction using data extracted from electronic health records (EHRs).

METHODS

Two EHR datasets containing data on 15,197 women from 2015 to 2018 at a single site, and 53,972 women from 2004 to 2017 at multiple sites were used as development and validation sets, respectively, to construct the PPD risk prediction model. The primary outcome was a diagnosis of PPD within 1 year following childbirth. A framework of data extraction, processing, and machine learning was implemented to select a minimal list of features from the EHR datasets to ensure model performance and to enable future point-of-care risk prediction.

RESULTS

The best-performing model uses from clinical features related to mental health history, medical comorbidity, obstetric complications, medication prescription orders, and patient demographic characteristics. The model performances as measured by area under the receiver operating characteristic curve (AUC) are 0.937 (95% CI 0.912 - 0.962) and 0.886 (95% CI 0.879-0.893) in the development and validation datasets, respectively. The model performances were consistent when tested using data ending at multiple time periods during pregnancy and at childbirth.

LIMITATIONS

The prevalence of PPD in the study data represented a treatment prevalence and is likely lower than the illness prevalence.

CONCLUSIONS

EHRs and machine learning offer the ability to identify women at risk for PPD early in their pregnancy. This may facilitate scalable and timely prevention and intervention, reducing negative outcomes and the associated burden.

Machine Learning-Based Predictive Modeling of Postpartum Depression

Postpartum depression is a serious health issue beyond the mental health problems that affect mothers after childbirth. There are no predictive tools available to screen postpartum depression that also allow early interventions. We aimed to develop predictive models for postpartum depression using machine learning (ML) approaches. We performed a retrospective cohort study using data from the Pregnancy Risk Assessment Monitoring System 2012–2013 with 28,755 records (3339 postpartum depression and 25,416 normal cases). The imbalance between the two groups was addressed by a balanced resampling using both random down-sampling and the synthetic minority over-sampling technique. Nine different ML algorithms, including random forest (RF), stochastic gradient boosting, support vector machines (SVM), recursive partitioning and regression trees, naïve Bayes, k-nearest neighbor (kNN), logistic regression, and neural network, were employed with 10-fold cross-validation to evaluate the models. The overall classification accuracies of the nine models ranged from 0.650 (kNN) to 0.791 (RF). The RF method achieved the highest area under the receiver-operating-characteristic curve (AUC) value of 0.884, followed by SVM, which achieved the second-best performance with an AUC value of 0.864. Predictive modeling developed using ML-approaches may thus be used as a prediction (screening) tool for postpartum depression in future studies.

Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study

Background

Postpartum depression (PPD) is a serious public health problem. Building a predictive model for PPD using data during pregnancy can facilitate earlier identification and intervention.

Objective

The aims of this study are to compare the effects of four different machine learning models using data during pregnancy to predict PPD and explore which factors in the model are the most important for PPD prediction.

Methods

Information on the pregnancy period from a cohort of 508 women, including demographics, social environmental factors, and mental health, was used as predictors in the models. The Edinburgh Postnatal Depression Scale score within 42 days after delivery was used as the outcome indicator. Using two feature selection methods (expert consultation and random forest-based filter feature selection [FFS-RF]) and two algorithms (support vector machine [SVM] and random forest [RF]), we developed four different machine learning PPD prediction models and compared their prediction effects.

Results

There was no significant difference in the effectiveness of the two feature selection methods in terms of model prediction performance, but 10 fewer factors were selected with the FFS-RF than with the expert consultation method. The model based on SVM and FFS-RF had the best prediction effects (sensitivity=0.69, area under the curve=0.78). In the feature importance ranking output by the RF algorithm, psychological elasticity, depression during the third trimester, and income level were the most important predictors.

Conclusions

In contrast to the expert consultation method, FFS-RF was important in dimension reduction. When the sample size is small, the SVM algorithm is suitable for predicting PPD. In the prevention of PPD, more attention should be paid to the psychological resilience of mothers.

Mobile Health for Perinatal Depression and Anxiety: Scoping Review

Background

The perinatal period is a vulnerable time during which depression and anxiety commonly occur. If left untreated or undertreated, there may be significant adverse effects; therefore, access to rapid, effective treatment is essential. Treatments for mild-to-moderate symptoms according to a stepped-care approach involve psychoeducation, peer support, and psychological therapy, all of which have been shown to be efficaciously delivered through digital means. Women experience significant barriers to care because of system- and individual-level factors, such as cost, accessibility, and availability of childcare. The use of mobile phones is widespread in this population, and the delivery of mental health services via mobile phones has been suggested as a means of reducing barriers.

Objective

This study aimed to understand the extent, range, and nature of mobile health (mHealth) tools for prevention, screening, and treatment of perinatal depression and anxiety in order to identify gaps and inform opportunities for future work.

Methods

Using a scoping review framework, 4 databases were searched for terms related to mobile phones, perinatal period, and either depression or anxiety. A total of 477 unique records were retrieved, 81 of which were reviewed by full text. Peer-reviewed publications were included if they described the population as women pregnant or up to 1 year postpartum and a tool explicitly delivered via a mobile phone for preventing, screening, or treating depression or anxiety. Studies published in 2007 or earlier, not in English, or as case reports were excluded.

Results

A total of 26 publications describing 22 unique studies were included (77% published after 2017). mHealth apps were slightly more common than texting-based interventions (12/22, 54% vs 10/22, 45%). Most tools were for either depression (12/22, 54%) or anxiety and depression (9/22, 41%); 1 tool was for anxiety only (1/22, 4%). Interventions starting in pregnancy and continuing into the postpartum period were rare (2/22, 9%). Tools were for prevention (10/22, 45%), screening (6/22, 27%), and treatment (6/22, 27%). Interventions delivered included psychoeducation (16/22, 73%), peer support (4/22, 18%), and psychological therapy (4/22, 18%). Cost was measured in 14% (3/22) studies.

Conclusions

Future work in this growing area should incorporate active psychological treatment, address continuity of care across the perinatal period, and consider clinical sustainability to realize the potential of mHealth.

Precision medicine in perinatal depression in light of the human microbiome

Perinatal depression is the most common complication of pregnancy and affects the mother, fetus, and infant. Recent preclinical studies and a limited number of clinical studies have suggested an influence of the gut microbiome on the onset and course of mental health disorders. In this review, we examine the current state of knowledge regarding genetics, epigenetics, heritability, and neuro-immuno-endocrine systems biology in perinatal mood disorders, with a particular focus on the interaction between these factors and the gut microbiome, which is mediated via the gut-brain axis. We also provide an overview of experimental and analytical methods that are currently available to researchers interested in elucidating the influence of the gut microbiome on mental health disorders during pregnancy and postpartum.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Mindfulness-Based Strategies: A Cost-Effective Stress Reduction Method for Parents in the NICU

Stress in parents who have an infant in the NICU is well documented in literature. Prematurity and related comorbid conditions, high-tech NICU environments, presence of multidisciplinary health care professionals, altered parenting roles, and concerns of health outcomes in the infant are common stress factors. Further, inadequate management of stress can result in poor parent-infant bonding, poor infant outcome, and postpartum depression in parents. Effective stress management strategies may help parents adapt to their parental role thereby improving infant outcomes. Research has shown mindfulness-based strategies help reduce stress in the general population. Can this strategy be applied in the context of parents of infants in the NICU? Literature is scant on the impact of mindfulness-based strategies on parents of infants in the NICU and on the infant's health outcomes. This article explores the application of mindfulness-based strategies to reduce stress in parents of infants in the NICU.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.

The Association Between the Use of Antenatal Care Smartphone Apps in Pregnant Women and Antenatal Depression: Cross-Sectional Study

Background

Antenatal care smartphone apps are increasingly used by pregnant women, but studies on their use and impact are scarce.

Objective

This study investigates the use of antenatal care apps in pregnant women and explores the association between the use of these apps and antenatal depression.

Methods

This study used a convenient sample of pregnant women recruited from Hunan Provincial Maternal and Child Health Hospital in November 2015. The participants were surveyed for their demographic characteristics, use of antenatal care apps, and antenatal depression. Factors that influenced antenatal pregnancy were analyzed using logistic regression.

Results

Of the 1304 pregnant women, 71.31% (930/1304) used antenatal care apps. Higher usage of apps was associated with urban residency, nonmigrant status, first pregnancy, planned pregnancy, having no previous children, and opportunity to communicate with peer pregnant women. The cutoff score of the Edinburgh Postnatal Depression Scale was 10, and 46.11% (601/1304) of the pregnant women had depression. Logistic regression showed that depression was associated with the availability of disease-screening functions in the apps (odds ratio (OR) 1.78, 95% CI 1.03-3.06) and spending 30 minutes or more using the app (OR 2.05, 95% CI 1.19-3.52). Using apps with social media features was a protective factor for antenatal depression (OR 0.33, 95% CI 0.12-0.89).

Conclusions

The prevalence of the use of prenatal care apps in pregnant women is high. The functions and time spent on these apps are associated with the incidence of antenatal depression.

eHealth as the Next-Generation Perinatal Care: An Overview of the Literature

Background

Unrestricted by time and place, electronic health (eHealth) provides solutions for patient empowerment and value-based health care. Women in the reproductive age are particularly frequent users of internet, social media, and smartphone apps. Therefore, the pregnant patient seems to be a prime candidate for eHealth-supported health care with telemedicine for fetal and maternal conditions.

Objective

This study aims to review the current literature on eHealth developments in pregnancy to assess this new generation of perinatal care.

Methods

We conducted a systematic literature search of studies on eHealth technology in perinatal care in PubMed and EMBASE in June 2017. Studies reporting the use of eHealth during prenatal, perinatal, and postnatal care were included. Given the heterogeneity in study methods, used technologies, and outcome measurements, results were analyzed and presented in a narrative overview of the literature.

Results

The literature search provided 71 studies of interest. These studies were categorized in 6 domains: information and eHealth use, lifestyle (gestational weight gain, exercise, and smoking cessation), gestational diabetes, mental health, low- and middle-income countries, and telemonitoring and teleconsulting. Most studies in gestational diabetes and mental health show that eHealth applications are good alternatives to standard practice. Examples are interactive blood glucose management with remote care using smartphones, telephone screening for postnatal depression, and Web-based cognitive behavioral therapy. Apps and exercise programs show a direction toward less gestational weight gain, increase in step count, and increase in smoking abstinence. Multiple studies describe novel systems to enable home fetal monitoring with cardiotocography and uterine activity. However, only few studies assess outcomes in terms of fetal monitoring safety and efficacy in high-risk pregnancy. Patients and clinicians report good overall satisfaction with new strategies that enable the shift from hospital-centered to patient-centered care.

Conclusions

This review showed that eHealth interventions have a very broad, multilevel field of application focused on perinatal care in all its aspects. Most of the reviewed 71 articles were published after 2013, suggesting this novel type of care is an important topic of clinical and scientific relevance. Despite the promising preliminary results as presented, we accentuate the need for evidence for health outcomes, patient satisfaction, and the impact on costs of the possibilities of eHealth interventions in perinatal care. In general, the combination of increased patient empowerment and home pregnancy care could lead to more satisfaction and efficiency. Despite the challenges of privacy, liability, and costs, eHealth is very likely to disperse globally in the next decade, and it has the potential to deliver a revolution in perinatal care.

Reducing procrastination using a smartphone-based treatment program: A randomized controlled pilot study

BACKGROUND

Procrastination affects a large number of individuals and is associated with significant mental health problems. Despite the deleterious consequences individuals afflicted with procrastination have to bear, there is a surprising paucity of well-researched treatments for procrastination. To fill this gap, this study evaluated the efficacy of an easy-to-use smartphone-based treatment for procrastination.

METHOD

N = 31 individuals with heightened procrastination scores were randomly assigned to a blended smartphone-based intervention including two brief group counseling sessions and 14 days of training with the mindtastic procrastination app (MT-PRO), or to a waitlist condition. MT-PRO fosters the approach of functional and the avoidance of dysfunctional behavior by systematically utilizing techniques derived from cognitive bias modification approaches, gamification principles, and operant conditioning. Primary outcome was the course of procrastination symptom severity as assessed with the General Procrastination Questionnaire.

RESULTS

Participating in the smartphone-based treatment was associated with a significantly greater reduction of procrastination than was participating in the control condition (η²  = .15).

CONCLUSION

A smartphone-based intervention may be an effective treatment for procrastination. Future research should use larger samples and directly compare the efficacy of smartphone-based interventions and traditional interventions for procrastination.

A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression

Depression is a common mental health condition for which many mobile apps aim to provide support. This review aims to identify self-help apps available exclusively for people with depression and evaluate those that offer cognitive behavioural therapy (CBT) or behavioural activation (BA). One hundred and seventeen apps have been identified after searching both the scientific literature and the commercial market. 10.26% (n = 12) of these apps identified through our search offer support that seems to be consistent with evidence-based principles of CBT or BA. Taking into account the non existence of effectiveness/efficacy studies, and the low level of adherence to the core ingredients of the CBT/BA models, the utility of these CBT/BA apps are questionable. The usability of reviewed apps is highly variable and they rarely are accompanied by explicit privacy or safety policies. Despite the growing public demand, there is a concerning lack of appropiate CBT or BA apps, especially from a clinical and legal point of view. The application of superior scientific, technological, and legal knowledge is needed to improve the development, testing, and accessibility of apps for people with depression.

Private Data Analytics on Biomedical Sensing Data via Distributed Computation

Advances in biomedical sensors and mobile communication technologies have fostered the rapid growth of mobile health (mHealth) applications in the past years. Users generate a high volume of biomedical data during health monitoring, which can be used by the mHealth server for training predictive models for disease diagnosis and treatment. However, the biomedical sensing data raise serious privacy concerns because they reveal sensitive information such as health status and lifestyles of the sensed subjects. This paper proposes and experimentally studies a scheme that keeps the training samples private while enabling accurate construction of predictive models. We specifically consider logistic regression models which are widely used for predicting dichotomous outcomes in healthcare, and decompose the logistic regression problem into small subproblems over two types of distributed sensing data, i.e., horizontally partitioned data and vertically partitioned data. The subproblems are solved using individual private data, and thus mHealth users can keep their private data locally and only upload (encrypted) intermediate results to the mHealth server for model training. Experimental results based on real datasets show that our scheme is highly efficient and scalable to a large number of mHealth users.