Integrating SMS Text Messages Into a Preventive Intervention for Postpartum Depression Delivered via In-Home Visitation Programs: Feasibility and Acceptability Study

Barrera, Alinne Z.; Hamil, Jaime; Tandon, Darius

doi:10.2196/30995

Cited by 7 publications

(6 citation statements)

References 42 publications

(44 reference statements)

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“…It is understandable that some mental health professionals contend that text messaging therapy becomes a need in many situations. As stated by previous literature, this is due to some limitations faced by the therapists or by the patients such as lack of technology accessibility, low technology, literacy and difficulties in reading and understanding text messages [ 52 , 53 , 54 ]. Second, the study also indicates that the choice of topic, word and sentences are important to be tailored to the patients’ background.…”

Section: Discussionmentioning

confidence: 99%

Text Messages for Depression, Anxiety and Alcohol Abuse Therapy—Are Construction Guidelines Needed?

Rahman

Nayan

2022

IJERPH

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Despite the effectiveness of text messaging therapy in improving mental health conditions, limited attention has been paid to how the text messages are constructed. Thus, this study questions whether there is a need to develop a model of text message construction for mental health therapy. In this backdrop, this study reviews how a text message for mental health therapy is constructed, specifically focused on the process and guidelines. This study also aims to identify the research gap regarding the guideline of text message construction for mental health therapy and to identify mental health professionals’ practices in text messaging therapy. In addition, the opinions of mental health professionals on the need to develop a text message construction guideline were also gathered. The findings from the literature review confirmed that there are still limited guidelines explaining the process of constructing text messages for mental health therapy. Meanwhile, results from the online survey found that mental health professionals expressed a high need to explore and develop a model of text message construction for mental health therapy. With this research gap addressed, this study proposes further research into the development of a text message construction model for mental health therapy.

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Section: Discussionmentioning

confidence: 99%

Text Messages for Depression, Anxiety and Alcohol Abuse Therapy—Are Construction Guidelines Needed?

Rahman

Nayan

2022

IJERPH

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“…In addition, participants received a JIT intervention, consisting of 4 SMS text messages, every other day at 7:45 PM on their mobile phones after each completed 1:1 intervention. The SMS text messages included brief messages and links to external content (eg, worksheets, videos, and guided mindfulness practices) and were designed to reinforce the most recent 1:1 intervention content and to encourage skill practice so that the skills become more frequently used to manage stress in one’s daily life [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Ng¹,

Wei²,

Jain³

et al. 2022

JMIR Mhealth Uhealth

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Background Cognitive behavioral therapy–based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can help to inform and enable pre-emptive interventions for a likely physiologically and perceptibly stressful day. Machine learning models are useful tools that can be developed to predict next-day physiological and perceived stress by using data collected from the previous day. Such models can improve our understanding of the specific factors that predict physiological and perceived stress and allow researchers to develop systems that collect selected features for assessment in clinical trials to minimize the burden of data collection. Objective The aim of this study was to build and evaluate a machine-learned model that predicts next-day physiological and perceived stress by using sensor-based, ecological momentary assessment (EMA)–based, and intervention-based features and to explain the prediction results. Methods We enrolled pregnant women into a prospective proof-of-concept study and collected electrocardiography, EMA, and cognitive behavioral therapy intervention data over 12 weeks. We used the data to train and evaluate 6 machine learning models to predict next-day physiological and perceived stress. After selecting the best performing model, Shapley Additive Explanations were used to identify the feature importance and explainability of each feature. Results A total of 16 pregnant women enrolled in the study. Overall, 4157.18 hours of data were collected, and participants answered 2838 EMAs. After applying feature selection, 8 and 10 features were found to positively predict next-day physiological and perceived stress, respectively. A random forest classifier performed the best in predicting next-day physiological stress (F1 score of 0.84) and next-day perceived stress (F1 score of 0.74) by using all features. Although any subset of sensor-based, EMA-based, or intervention-based features could reliably predict next-day physiological stress, EMA-based features were necessary to predict next-day perceived stress. The analysis of explainability metrics showed that the prolonged duration of physiological stress was highly predictive of next-day physiological stress and that physiological stress and perceived stress were temporally divergent. Conclusions In this study, we were able to build interpretable machine learning models to predict next-day physiological and perceived stress, and we identified unique features that were highly predictive of next-day stress that can help to reduce the burden of data collection.

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“…On a given day, a participant who was determined to be stressed would only receive 1 JIT message from a particular session. If a participant’s most recent session was doubled up due to scheduling, they would receive 1 JIT message from each of the prior sessions (Barrera et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…The W-4-2 research team adapted the Mothers and Babies (MB) intervention that includes 12 individual sessions targeted to women at 10–22 weeks gestation to reduce prenatal distress (Muñoz et al, 2007 ). By adding just-in-time (JIT) content to supplement the intervention, the W-4-2 team reinforced material covered during the 12 sessions to further improve efficacy (Barrera et al, 2021 ). The W-4-2 team designed the JIT component with daily signals provided by ECG data collected by a wearable biosensor device and self-reported perceived stress from EMAs (Wakschlag et al, 2021 ).…”

mentioning

confidence: 99%

Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake

et al. 2022

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Researchers increasingly use passive sensing data and frequent self-report to implement personalized mobile health (mHealth) interventions. Yet, we know that certain populations may find these technical protocols burdensome and intervention uptake as well as treatment efficacy may be affected as a result. In the present study, we predicted feasibility (participant adherence to protocol) and acceptability (participant engagement with intervention content) as a function of baseline sociodemographic, mental health, and well-being characteristics of 99 women randomized in the personalized preventive intervention Wellness-for-Two (W-4-2), a randomized trial evaluating stress-related alterations during pregnancy and their effect on infant neurodevelopmental trajectories. The W-4-2 study used ecological momentary assessment (EMA) and wearable electrocardiograph (ECG) sensors to detect physiological stress and personalize the intervention. Participant adherence to protocols was 67% for EMAs and 52% for ECG bio-sensors. Higher baseline negative affect significantly predicted lower adherence to both protocols. Women assigned to the intervention group engaged on average with 42% of content they received. Women with higher annual household income were more likely to engage with more of the intervention content. Researchers should carefully consider tailoring of the intensity of technical intervention protocols to reduce fatigue, especially among participants with higher baseline negative affect, which may improve intervention uptake and efficacy findings at scale.

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Integrating SMS Text Messages Into a Preventive Intervention for Postpartum Depression Delivered via In-Home Visitation Programs: Feasibility and Acceptability Study

Cited by 7 publications

References 42 publications

Text Messages for Depression, Anxiety and Alcohol Abuse Therapy—Are Construction Guidelines Needed?

Text Messages for Depression, Anxiety and Alcohol Abuse Therapy—Are Construction Guidelines Needed?

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake

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