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; Heerden, Alastair van; Hagaman, Ashley; Maharjan, Sujen Man; Byanjankar, Prabin; Subba, Prasansa; Kohrt, Brandon A.

doi:10.2196/14734

Cited by 26 publications

(55 citation statements)

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“…In case of mother-child interaction, we suggest using additional observation tools such as HOME (27,45,46) and OMCI (47)(48)(49) which have been used in multi-cultural settings to assess mother and child's social environment, interactions and support. Finally, additional methods of passive data collection, such as Bluetooth beacons attached to child's clothing can help determine the time mother and child spend together (19,29).…”

Section: Discussionmentioning

confidence: 99%

“…While domains such as interactions can be assessed by the presence or absence of human speech, domains like support might require total number and frequency of speech, time-speci c contact, predictability of interactions and amount of audio stimuli observed/expected. Passive audio data when combined with appropriate qualitative and observational tools such as daily diary elicitation, ecological momentary assessment, HOME, Quality of mother-child interaction, and additional passive data collection methods like Global Positioning System (GPS) (50), Bluetooth beacons (19,29), sleep pattern detection (43), accelerometer (38,44), including recording call logs (44) and application usages (57), and device activity (58) can provide unique and innovative insight into mother's social environment.…”

Section: Discussionmentioning

confidence: 99%

“…This current study was part of a broader initiative to improve mental health services in rural Nepal (21), including a special focus on use of technology to improve maternal mental health for adolescents (19).We conducted the study in Chitwan, a southern region of Nepal with a population of 579,984. Chitwan has an under 5 mortality rate of 38.6 per 1000 and a literacy rate of 78.9% (22).…”

Section: Settingmentioning

confidence: 99%

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What does social support sound like? Challenges and opportunities for using passive episodic audio collection to assess the social environment

Poudyal¹,

Heerden

Hagaman

et al. 2020

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Background: The social environment, including social support, social burden, and quality of interactions, influences a range of health outcomes, including mental health. Passive audio data collection on mobile phones (e.g., episodic recording of the auditory environment without requiring any active input from the phone user) enables new opportunities to understand the social environment. We evaluated the use of passive audio collection on mobile phones as a window onto the relationship between the social environment within a study of mental health among adolescent mothers in Nepal.Methods: We enrolled 23 adolescent mothers who first participated in qualitative interviews to describe their social support and identify sounds potentially associated with that support. Then episodic recordings were collected for two weeks from the same women using an app to capture 30 seconds of audio every 15 minutes from 4am to 9pm. Audio data were processed and classified using a pretrained model. Each classification category was accompanied by a predicted accuracy score. Manual validation of the machine-predicted speech and non-speech categories (10%) was done for accuracy.Results: In qualitative interviews, mothers described a range of positive and negative social interactions and the sounds that accompanied these. Potential positive sounds included adult speech and laughter, baby babbling and laughter, and sounds from baby toys. Sounds characterizing negative stimuli included yelling, crying, screaming by adults and crying by babies. Sounds associated with social isolation included silence and TV or radio noises. Speech comprised of 43% of all passively recorded audio clips (n=7725). Manual validation showed a 23% false positive rate and 62% false-negative rate for speech, demonstrating potential underestimation of speech exposure. Other common sounds included music and vehicular noises.Conclusions: Passively capturing audio has the potential to improve understanding of the social environment. However, the limited accuracy of the pre-trained model used in this study did not adequately distinguish between positive and negative social interactions. To improve the contribution of passive audio collection to understanding the social environment, future work should improve the accuracy of audio categorization, code for constellations of sounds, and combine audio with other smartphone data collection such as location and activity.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Settingmentioning

confidence: 99%

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What does social support sound like? Challenges and opportunities for using passive episodic audio collection to assess the social environment

Poudyal¹,

Heerden

Hagaman

et al. 2020

Preprint

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“…Data comes from the pilot study, Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) [53]. The development, pilot protocol, and feasibility ndings of StandStrong are described in detail elsewhere [53,54] and is registered with a International Registered Report Identi er (IRRID): DERR1-10.2196/14734. Below, we describe the elements speci c to this analysis.…”

Section: Methodsmentioning

confidence: 99%

Adolescent motherhood in rural Nepal: passive sensing data illuminates daily experiences in maternal lives

Hagaman¹,

Mercado²,

Poudyal³

et al. 2020

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Background: Adolescent pregnancy, particularly in low-income settings, is associated with adverse health and social outcomes for both mother and child. Nepal has the second highest rate of adolescent pregnancy in South Asia alongside high rates of maternal depression and suicide. While the maternal morbidity risks of adolescent pregnancy are well researched, impacts on everyday lives, including behaviors and predictable patterns are less well-known. Passive sensing (geographic movement, physical activity, and proximity to infants using Bluetooth technology) is an emerging technology that can enhance the detection of behavior patterns. Given the risk of the postpartum period in LMIC settings, we sought to characterize normal behavioral patterns via passive sensing technology. Methods: We collected passive data over a two-week period with 22 mothers using phone-based GPS, accelerometry, and Bluetooth technologies. Passive data was aggregated for each mother, collapsed into hourly readings, and descriptively summarized. We triangulated this information in a constant comparative approach with a range of qualitative data including multiple in-depth interviews, a daily diary, and systematic fieldnotes. Results: Passive data illuminated a range of behaviors that varied across our participants. Women, during the average time window of 4am and 8pm, spent more than 80% of the day with their infants, were detected as ‘active’ 10-20% of the time in any given hour with peaks in the morning and mid-afternoon, and traveled fewer than 1675 meters from their homes. Household work, instrumental childcare, and household support, paired with the infant’s age, appeared to drive activity patterns. Women with higher amounts of activity and GPS movement had more household support for chores and childcare. Women with young infants had smaller amounts of activity and GPS movement. Women that had nearly no time away from their infant expressed overwhelming responsibilities and increased stress, but also role fulfillment in that time with their infant was an indicator of good mothering. Conclusion: We reveal typical behavioral patterns of rural adolescent mothers and highlight opportunities for integrating this information to improve health and well-being.

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“…The study protocol is outlined in detail elsewhere, and we describe it briefly below; the procedures and results described here refer to Component 2 of the original study protocol [17]. Mothers were enrolled in the study for 14 days.…”

Section: Study Proceduresmentioning

confidence: 99%

Passive Sensing Data Collection with Adolescent Mothers and Their Infants to Improve Mental Health Services in Low-Resource Settings: A Feasibility and Acceptability Study in Rural Nepal

Maharjan¹,

Poudyal

Heerden

et al. 2020

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Background: Passive sensor data from mobile phones can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. Our Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) study assessed feasibility and acceptability of passive data collection with young mothers, including mothers experiencing postpartum depression, in rural Nepal.Methods: Mothers between 15-25 years of age with infants less than 12 months old were recruited from vaccination clinics in rural Nepal. They were provided with a mobile smartphone and passive Bluetooth beacon to collect data in four domains: the mother’s location using the Global Positioning System (GPS), physical activity using the phone’s accelerometer, auditory environment using episodic audio recording on the phone, and mother-infant proximity measured with Bluetooth beacon attached to the infant’s clothing. Feasibility and acceptability were evaluated based on the amount of passive sensing data collected compared to the total amount that could be collected in a 2-week period. End-line qualitative interviews (n=31) were conducted to understand mothers’ experiences and perceptions of passive data collection.Results: 782 women were approached and 320 met eligibility criteria. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Of 9,602 possible readings per sensor, 57.4% of audio (5,579 recordings), 50.6% of activity (5,001 readings), 41.1% of proximity (4,168 readings), and 35.4% of GPS (3,482 readings) were obtained. The percentage of data collection was comparable for depressed and non-depressed mothers. Qualitative interviews revealed mobile charging, excessive data usage, and burden of carrying mobile phones as feasibility challenges. Concerns for privacy and family involvement were acceptability challenges. Overall, study team engagement and education of family members on technology contributed to mothers’ comfort participating in passive data collection. Conclusion: Approximately half of all possible passive data were collected. Feasibility challenges can be addressed by providing alternative phone charging options, setting up reverse billing for the app, and exploring smartwatches as replacement for mobile phones. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing data collection for psychological treatments and other health services. Registration: International Registered Report Identifier (IRRID): DERR1-10.2196/14734

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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

Cited by 26 publications

References 77 publications

What does social support sound like? Challenges and opportunities for using passive episodic audio collection to assess the social environment

What does social support sound like? Challenges and opportunities for using passive episodic audio collection to assess the social environment

Adolescent motherhood in rural Nepal: passive sensing data illuminates daily experiences in maternal lives

Passive Sensing Data Collection with Adolescent Mothers and Their Infants to Improve Mental Health Services in Low-Resource Settings: A Feasibility and Acceptability Study in Rural Nepal

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