Dataset Information

Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal.

ABSTRACT:

Background

There is a high prevalence of untreated postpartum depression among adolescent mothers with the greatest gap in services in low- and middle-income countries. Recent studies have demonstrated the potential of nonspecialists to provide mental health services for postpartum depression in these low-resource settings. However, there is inconsistency in short-term and long-term benefits from the interventions. Passive sensing data generated from wearable digital devices can be used to more accurately distinguish which mothers will benefit from psychological services. In addition, wearable digital sensors can be used to passively collect data to personalize care for mothers. Therefore, wearable passive sensing technology has the potential to improve outcomes from psychological treatments for postpartum depression.

Objective

This study will explore the use of wearable digital sensors for two objectives: First, we will pilot test using wearable sensors to generate passive sensing data that distinguish adolescent mothers with depression from those without depression. Second, we will explore how nonspecialists can integrate data from passive sensing technologies to better personalize psychological treatment.

Methods

This study will be conducted in rural Nepal with participatory involvement of adolescent mothers and health care stakeholders through a community advisory board. The first study objective will be addressed by comparing behavioral patterns of adolescent mothers without depression (n=20) and with depression (n=20). The behavioral patterns will be generated by wearable digital devices collecting data in 4 domains: (1) the physical activity of mothers using accelerometer data on mobile phones, (2) the geographic range and routine of mothers using GPS (Global Positioning System) data collected from mobile phones, (3) the time and routine of adolescent mothers with their infants using proximity data collected from Bluetooth beacons, and (4) the verbal stimulation and auditory environment for mothers and infants using episodic audio recordings on mobile phones. For the second objective, the same 4 domains of data will be collected and shared with nonspecialists who are delivering an evidence-based behavioral activation intervention to the depressed adolescent mothers. Over 5 weeks of the intervention, we will document how passive sensing data are used by nonspecialists to personalize the intervention. In addition, qualitative data on feasibility and acceptability of passive data collection will be collected for both objectives.

Results

To date, a community advisory board comprising young women and health workers engaged with adolescent mothers has been established. The study is open for recruitment, and data collection is anticipated to be completed in November 2019.

Conclusions

Integration of passive sensing data in public health and clinical programs for mothers at risk of perinatal mental health problems has the potential to more accurately identify who will benefit from services and increase the effectiveness by personalizing psychological interventions.

International registered report identifier (irrid)

DERR1-10.2196/14734.

SUBMITTER: Poudyal A

PROVIDER: S-EPMC6746061 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal.

Poudyal Anubhuti A van Heerden Alastair A Hagaman Ashley A Maharjan Sujen Man SM Byanjankar Prabin P Subba Prasansa P Kohrt Brandon A BA

JMIR research protocols 20190911 8

<h4>Background</h4>There is a high prevalence of untreated postpartum depression among adolescent mothers with the greatest gap in services in low- and middle-income countries. Recent studies have demonstrated the potential of nonspecialists to provide mental health services for postpartum depression in these low-resource settings. However, there is inconsistency in short-term and long-term benefits from the interventions. Passive sensing data generated from wearable digital devices can be used ...[more]

PMID: 31512581

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Project description:Background:Postpartum contraceptives use offers a distinctive opportunity to maintain appropriate birth spacing for health benefits to both mother and child. However, the concept of postpartum family planning (PPFP) is poorly understood and contraceptives use during the postpartum period remains low in Nepal. Therefore, this study aimed to assess the factors affecting the utilization of family planning (FP) methods among postpartum mothers in the Kailali district, Nepal. Methods:A community-based cross-sectional study was conducted between September and October 2013 among postpartum mothers in Kailali district. Simple random sampling was applied to select a total of 427 study participants. A pre-tested standard semi-structured questionnaire was used for gathering data. Data were analyzed using Statistical Package for Social Sciences (SPSS) version 20.0. Results:Overall, 32.8% of the postpartum women used different types of contraceptives. Condoms (37.1%) followed by Depo-Provera 29.3% were the most used postpartum contraceptives. Half (50%) of the postpartum women had an unmet need for family planning. Multiple logistic regression model revealed that the occupation of husband [AOR=3.2 (95% CI: 2.0-6.00], past use of family planning methods [AOR=4.0 (95% CI:2.4-6.5)] and resumption of menstruation [AOR=2.5 (95% CI:1.6-4.1)] were significantly associated with the use of modern contraceptives during the first year of the postpartum period. Conclusion:Contraceptive uptake within the first year of postpartum was found to be low among women in the Kailali district, Nepal. Previous experience with the use of family planning methods, occupation of the husbands, and resumption of menstruation are important factors in the PPFP decisions of women in this population. Therefore, the family planning program should work on improving couples' knowledge of the risk of pregnancy, fertility returning time and modern contraceptives use during the postpartum period targeting women whose husbands are in migrant occupations, who are not menstruating and who have not used FP before.

Project description:BackgroundDepression levels in adolescents have trended upward over the past several years. According to a 2020 survey by the National Survey on Drug Use and Health, 4.1 million US adolescents have experienced at least one major depressive episode. This number constitutes approximately 16% of adolescents aged 12 to 17 years. However, only 32.3% of adolescents received some form of specialized or nonspecialized treatment. Identifying worsening symptoms earlier using mobile and wearable sensors may lead to earlier intervention. Most studies on predicting depression using sensor-based data are geared toward the adult population. Very few studies look into predicting depression in adolescents.ObjectiveThe aim of our work was to study passively sensed data from adolescents with depression and investigate the predictive capabilities of 2 machine learning approaches to predict depression scores and change in depression levels in adolescents. This work also provided an in-depth analysis of sensor features that serve as key indicators of change in depressive symptoms and the effect of variation of data samples on model accuracy levels.MethodsThis study included 55 adolescents with symptoms of depression aged 12 to 17 years. Each participant was passively monitored through smartphone sensors and Fitbit wearable devices for 24 weeks. Passive sensors collected call, conversation, location, and heart rate information daily. Following data preprocessing, 67% (37/55) of the participants in the aggregated data set were analyzed. Weekly Patient Health Questionnaire-9 surveys answered by participants served as the ground truth. We applied regression-based approaches to predict the Patient Health Questionnaire-9 depression score and change in depression severity. These approaches were consolidated using universal and personalized modeling strategies. The universal strategies consisted of Leave One Participant Out and Leave Week X Out. The personalized strategy models were based on Accumulated Weeks and Leave One Week One User Instance Out. Linear and nonlinear machine learning algorithms were trained to model the data.ResultsWe observed that personalized approaches performed better on adolescent depression prediction compared with universal approaches. The best models were able to predict depression score and weekly change in depression level with root mean squared errors of 2.83 and 3.21, respectively, following the Accumulated Weeks personalized modeling strategy. Our feature importance investigation showed that the contribution of screen-, call-, and location-based features influenced optimal models and were predictive of adolescent depression.ConclusionsThis study provides insight into the feasibility of using passively sensed data for predicting adolescent depression. We demonstrated prediction capabilities in terms of depression score and change in depression level. The prediction results revealed that personalized models performed better on adolescents than universal approaches. Feature importance provided a better understanding of depression and sensor data. Our findings can help in the development of advanced adolescent depression predictions.

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Dataset Information

Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal.

Background

Objective

Methods

Results

Conclusions

International registered report identifier (irrid)

Publications

Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal.

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