Methods in Digital Mental Health: Smartphone-Based Assessment and Intervention for Stress, Anxiety, and Depression

Kolenik, Tine

doi:10.1007/978-3-030-91181-2_7

Cited by 7 publications

(7 citation statements)

References 102 publications

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“…Network feature extraction is performed based on CNN. The overall framework of the designed CNN is drawn in Figure 3 [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Liu

2022

Computational Intelligence and Neuroscience

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The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM) and the main contents of PSE student-oriented mental health quality cultivation under deep learning (DL). Secondly, an MHM is implemented based on big data technology (BDT) and the convolutional neural network (CNN). Simultaneously, the long short-term memory (LSTM) is introduced to optimize the proposed MHM. Finally, the performance of the MHM before and after optimization is evaluated, and the PSE student-oriented mental health quality training strategy based on the proposed MHM is offered. The results show that the accuracy curve is higher than the recall curve in all classification algorithms. The maximum recall rate is 0.58, and the minimum accuracy rate is 0.62. The decision tree (DT) algorithm has the best comprehensive performance among the five different classification algorithms, with accuracy of 0.68, recall rate of 0.58, and F1-measure of 0.69. Thus, the DT algorithm is selected as the classifier. The proposed MHM can identify 56% of students with MDs before optimization. After optimization, the accuracy is improved by 0.03. The recall rate is improved by 0.19, the F1-measure is improved by 0.05, and 75% of students with MDs can be identified. Diverse behavior data can improve the recognition effect of students’ MDs. Meanwhile, from the 60th iteration, the mode accuracy and loss tend to be stable. By comparison, batch_size has little influence on the experimental results. The number of convolution kernels of the first convolution layer has little influence. The proposed MHM based on DL and CNN will indirectly improve the mental health quality of PSE students. The research provides a reference for cultivating the mental health quality of PSE students.

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“…Network feature extraction is performed based on CNN. The overall framework of the designed CNN is drawn in Figure 3 [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Liu

2022

Computational Intelligence and Neuroscience

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“…Recognizing that mental health issues are on the risen, affecting individuals and society in increasingly complex ways and that existing human resources are insufficient to address the crisis, decision-makers have turned to technology to see what opportunities it may provide. The role of IoT-enabled technology in this new digital mental health landscape can be divided into two complementary processes: assessment and intervention [18].…”

Section: Related Workmentioning

confidence: 99%

“…Ref Goals Contribution 1 [18] Identify approaches in digital mental health Distinguished technology for mental health diagnosis into two complementary processes: assessment and intervention 2…”

Section: S/nmentioning

confidence: 99%

An Improved Bagging Ensemble in Predicting Mental Disorder using Hybridized Random Forest - Artificial Neural Network Model

Adeniji

Adeyemi²,

Ajagbe

2022

IJCAI

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Learning majorly provides the process of collecting, identifying, pre-processing, training, validating and visualization of data. This study identifies the problem of late detection of mental disorders in IT employees. There are many cases of mental disorders that are not apparent, notable or diagnosed until they become critical. This affects the productivity of the employees not only in the information technology (IT) industry. The objective of the study is to develop a Hybrid Random Forest (RF) and Artificial Neural Network (ANN) model to predict mental health disorders among employees in the IT industry. The experiment applied a hybrid Random Forest and Artificial Neural Network (RF-ANN) model in predicting the chances of IT employees developing mental disorders. To measure the performance of the model, RF and ANN algorithms were separately developed, their results were recorded and compared with the results of the hybrid model. In the hybrid model using "Bagging Ensemble," the prediction of an IT employee developing a Mental Disorder shows the weighted average performance of 84.5% for precision, recall, and accuracy and precision is 82.5% using the hybridized RF and ANN models on "Bagging Ensemble". This result obtained from the hybrid model correctly shows a significant improvement in its performance over individual performances of the RF model and ANN models. There was a marginal improvement in the performance of the hybrid model when compared with the result of the parameter-tuned RF. This suggests that by applying the RF-ANN model an improved dataset could be investigated and compared with the results obtained in this study.Povzetek: Članek se ukvarja z napovedovanjem mentalnih težav s pomočjo metod globokih nevronskih mrež, naključnih gozdov in vrečastega ansambla.

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“…30 In addition, many people always carry their smartphones and even use them instead of computers to access the internet. 30,31 These smartphone features could be used in ambulatory care and rural populations if mental health expertise and equipment are not available. In addition, the SPSRS application is free of charge and enables smartphones to process interventions at any time without restrictions.…”

Section: Introductionmentioning

confidence: 99%

“…The SPSRS application can address the three availability issues in mental health interventions: location-based availability (direct physical access to treatment), time-based availability (inability to receive needed treatment due to time of day), and cost-based availability (inability to access services beyond the recommended minimum weekly hours). [31][32][33][34][35] Secondly, the SPSRS application uses the YouTube Application Programming Interface. This makes it possible to watch videos uploaded on YouTube with the SPSRS application.…”

Section: Introductionmentioning

confidence: 99%

Immediate Effects of Mobile Phone App for Depressed Mood in Young Adults with Subthreshold Depression: A Pilot Randomized Controlled Trial

Ejiri,

Uchida,

Tsuchiya

et al. 2023

NDT

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Background: Preventive interventions for subthreshold depression (StD) are essential to reduce the incidence of major depressive disorder. Our smartphone application presenting positive word stimulation in video (ie, Subliminal Priming with Supraliminal Reward Stimulation, SPSRS) was suggested to improve depressive symptoms in people with StD, although it is unclear whether it can immediately improve depressed mood. This pilot randomized controlled trial (RCT) aimed to investigate the preliminary efficacy of SPSRS application intervention on depressive mood in people with StD. Methods: Thirty-two participants with StD were randomly assigned to the experimental (n = 16) or control group (n = 16). The experimental group received SPSRS application intervention (10-minute video with positive word stimulation) and the control group received YouTube application intervention (10-minute video without positive word stimulation). Both groups used identical iPhones managed by the research team. The primary outcome was the change from baseline in depression-dejection on the Profile of Mood States 2nd Edition-Adult Short (POMS 2-A Short) after the intervention. Results: No participants dropped out of the study. The experimental group showed a small improvement in depression-dejection on the POMS 2-A Short score (adjusted Hedges's g = −0.32) compared to the control group. Post-hoc power analyses estimated a sample size of 56 per group (112 total) to evaluate depression-dejection on the POMS 2-A Short in a future full-scale RCT. Conclusion: SPSRS application intervention may be effective in immediately improving depressive mood in people with StD. A future full-scale RCT based on a formally calculated sample size should be conducted to replicate these findings.

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Methods in Digital Mental Health: Smartphone-Based Assessment and Intervention for Stress, Anxiety, and Depression

Cited by 7 publications

References 102 publications

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

Deep Learning-Based Mental Health Model on Primary and Secondary School Students’ Quality Cultivation

An Improved Bagging Ensemble in Predicting Mental Disorder using Hybridized Random Forest - Artificial Neural Network Model

Immediate Effects of Mobile Phone App for Depressed Mood in Young Adults with Subthreshold Depression: A Pilot Randomized Controlled Trial

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