scholarly journals 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

2020 ◽  
Author(s):  
Sujen Man Maharjan ◽  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Prabin Byanjankar ◽  
Ada Thapa ◽  
...  
Keyword(s):  
Data Collection ◽  
Health Services ◽  
Family Involvement ◽  
Mobile Phones ◽  
Qualitative Interviews ◽  
Low Resource Settings ◽  
Sensing Data ◽  
Passive Sensing ◽  
Passive Data ◽  
Rural Nepal

Abstract 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

scholarly journals Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sujen Man Maharjan ◽  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Prabin Byanjankar ◽  
Ada Thapa ◽  
...  
Keyword(s):  
Data Collection ◽  
Health Services ◽  
Mobile Devices ◽  
Family Involvement ◽  
Mobile Phones ◽  
Young Mothers ◽  
Audio Recording ◽  
Low Resource Settings ◽  
Passive Sensing ◽  
Passive Data

Abstract Background Passive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal. Methods Mothers (15–25 years old) with infants (< 12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a 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 the 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. Endline qualitative interviews were conducted to understand mothers’ experiences and perceptions of passive data collection. Results Of the 782 women approached, 320 met eligibility criteria and 38 mothers (11 depressed, 27 non-depressed) were enrolled. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Across all participants, 5,579 of the hour-long data collection windows had at least one audio recording [mean (M) = 57.4% of the total possible hour-long recording windows per participant; median (Mdn) = 62.6%], 5,001 activity readings (M = 50.6%; Mdn = 63.2%), 4,168 proximity readings (M = 41.1%; Mdn = 47.6%), and 3,482 GPS readings (M = 35.4%; Mdn = 39.2%). Feasibility challenges were phone battery charging, data usage exceeding prepaid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families’ understanding of passive sensing and families’ awareness of potential benefits to mothers and infants were the major modifiable factors increasing acceptability and reducing gaps in data collection. Conclusion Per sensor type, approximately half of the hour-long collection windows had at least one reading. Feasibility challenges for passive sensing on mobile devices can be addressed by providing alternative phone charging options, reverse billing for the app, and replacing mobile phones with smartwatches. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services. Registration International Registered Report Identifier (IRRID): DERR1-10.2196/14734

scholarly journals Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability 

2020 ◽  
Author(s):  
Sujen Man Maharjan ◽  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Prabin Byanjankar ◽  
Ada Thapa ◽  
...  
Keyword(s):  
Data Collection ◽  
Health Services ◽  
Mobile Devices ◽  
Family Involvement ◽  
Mobile Phones ◽  
Young Mothers ◽  
Low Resource Settings ◽  
Low Resource ◽  
Passive Sensing ◽  
Passive Data

Abstract Background: Passive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal.Methods: Mothers (15-25 years old) with infants (<12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a 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 the 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. Endline 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 9,605 possible readings per sensor, 5,579 audio recordings [mean (M)=57.4%; median (Mdn)=62.6%], 5,001 activity readings (M=50.6%; Mdn=63.2%), 4,168 proximity readings (M=41.1%; Mdn=47.6%), and 3,482 GPS readings (M=35.4%; Mdn=39.2%) were obtained. Feasibility challenges were phone battery charging, data usage exceeding pre-paid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families’ understanding of passive sensing and families’ awareness of potential benefits to mothers and infants were the major modifiable factors to increase acceptability and reduce gaps in data collection. Conclusion: Approximately half of all possible passive data readings were collected. Feasibility challenges can be addressed by providing alternative phone charging options, setting up reverse billing for the app, and exploring smartwatches as a replacement for mobile phones. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services. Registration: International Registered Report Identifier (IRRID): DERR1-10.2196/14734

scholarly journals 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 (Preprint)

2019 ◽  
Author(s):  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Ashley Hagaman ◽  
Sujen Man Maharjan ◽  
Prabin Byanjankar ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Mobile Phones ◽  
Adolescent Mothers ◽  
Psychological Treatment ◽  
Advisory Board ◽  
Community Advisory Board ◽  
Sensing Data ◽  
Digital Devices ◽  
Passive Sensing ◽  
Rural Nepal

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.

scholarly journals 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

10.2196/14734 ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. e14734 ◽  
Author(s):  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Ashley Hagaman ◽  
Sujen Man Maharjan ◽  
Prabin Byanjankar ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Mobile Phones ◽  
Adolescent Mothers ◽  
Psychological Treatment ◽  
Advisory Board ◽  
Community Advisory Board ◽  
Sensing Data ◽  
Digital Devices ◽  
Passive Sensing ◽  
Rural Nepal

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

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

2020 ◽  
Author(s):  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Ashley Hagaman ◽  
Celia Islam ◽  
Ada Thapa ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Data Collection ◽  
Social Interactions ◽  
Social Environment ◽  
Mobile Phones ◽  
Adolescent Mothers ◽  
Qualitative Interviews ◽  
The Social ◽  
Audio Data

Abstract 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.

scholarly journals Utilizing passive sensing data to provide personalized psychological care in low-resource settings

2021 ◽  
Vol 4 ◽  
pp. 118
Author(s):  
Prabin Byanjankar ◽  
Anubhuti Poudyal ◽  
Brandon A Kohrt ◽  
Sujen Man Maharjan ◽  
Ashley Hagaman ◽  
...  
Keyword(s):  
Mental Health ◽  
Adolescent Mothers ◽  
Behavioral Patterns ◽  
Proximity Data ◽  
Low Resource Settings ◽  
Lay Counselors ◽  
Sensing Data ◽  
Low Resource ◽  
Counseling Intervention ◽  
Passive Sensing

Background: With the growing ubiquity of smartphones and wearable devices, there is an increased potential of collecting passive sensing data in mobile health. Passive data such as physical activity, Global Positioning System (GPS), interpersonal proximity, and audio recordings can provide valuable insight into the lives of individuals. In mental health, these insights can illuminate behavioral patterns, creating exciting opportunities for mental health service providers and their clients to support pattern recognition and problem identification outside of formal sessions. In the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) project, our aim was to build an mHealth application to facilitate the delivery of psychological treatments by lay counselors caring for adolescent mothers with depression in Nepal. Methods: This paper describes the development of the StandStrong platform comprising the StandStrong Counselor application, and a cloud-based processing system, which can incorporate any tool that generates passive sensing data. We developed the StandStrong Counselor application that visualized passively collected GPS, proximity, and activity data. In the app, GPS data displays as heat maps, proximity data as charts showing the mother and child together or apart, and mothers’ activities as activity charts. Lay counselors can use the StandStrong application during counseling sessions to discuss mothers’ behavioral patterns and clinical progress over the course of a five-week counseling intervention. Achievement Awards based on collected data can also be automatically generated and sent to mothers. Additionally, messages can be sent from counselors to mother’s personal phones through the StandStrong platform. Discussion: The StandStrong platform has the potential to improve the quality and effectiveness of psychological services delivered by non-specialists in diverse global settings.

scholarly journals Utilizing passive sensing data to provide personalized psychological care in low-resource settings

2020 ◽  
Vol 4 ◽  
pp. 118
Author(s):  
Prabin Byanjankar ◽  
Anubhuti Poudyal ◽  
Brandon A Kohrt ◽  
Sujen Man Maharjan ◽  
Ashley Hagaman ◽  
...  
Keyword(s):  
Mental Health ◽  
Adolescent Mothers ◽  
Behavioral Patterns ◽  
Proximity Data ◽  
Low Resource Settings ◽  
Lay Counselors ◽  
Sensing Data ◽  
Low Resource ◽  
Counseling Intervention ◽  
Passive Sensing

Background: With the growing ubiquity of smartphones and wearable devices, there is an increased potential of collecting passive sensing data in mobile health. Passive data such as physical activity, Global Positioning System (GPS), interpersonal proximity, and audio recordings can provide valuable insight into the lives of individuals. In mental health, these insights can illuminate behavioral patterns, creating exciting opportunities for mental health service providers and their clients to support pattern recognition and problem identification outside of formal sessions. In the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) project, our aim was to build an mHealth application to facilitate the delivery of psychological treatments by lay counselors caring for adolescent mothers with depression in Nepal. Methods: This paper describes the development of the StandStrong platform comprising the StandStrong Counselor application, and a cloud-based processing system, which can incorporate any tool that generates passive sensing data. We developed the StandStrong Counselor application that visualized passively collected GPS, proximity, and activity data. In the app, GPS data displays as heat maps, proximity data as charts showing the mother and child together or apart, and mothers’ activities as activity charts. Lay counselors can use the StandStrong application during counseling sessions to discuss mothers’ behavioral patterns and clinical progress over the course of a five-week counseling intervention. Awards based on collected data also can be automatically generated and sent to mothers. Additionally, messages can be sent from counselors to mother’s personal phones through the StandStrong platform. Discussion: The StandStrong platform has the potential to improve the quality and effectiveness of psychological services delivered by non-specialists in diverse global settings.

scholarly journals Procedures to Select Digital Sensing Technologies for Passive Data Collection With Children and Their Caregivers: Qualitative Cultural Assessment in South Africa and Nepal (Preprint)

2018 ◽  
Author(s):  
Brandon A Kohrt ◽  
Sauharda Rai ◽  
Khanya Vilakazi ◽  
Kiran Thapa ◽  
Anvita Bhardwaj ◽  
...  
Keyword(s):  
South Africa ◽  
Data Collection ◽  
Community Health ◽  
Health Workers ◽  
Time Lapse ◽  
Home Based ◽  
Passive Sensing ◽  
Passive Data ◽  
Environmental Sensor ◽  
Cultural Assessment

BACKGROUND Populations in low-resource settings with high childhood morbidity and mortality increasingly are being selected as beneficiaries for interventions using passive sensing data collection through digital technologies. However, these populations often have limited familiarity with the processes and implications of passive data collection. Therefore, methods are needed to identify cultural norms and family preferences influencing the uptake of new technologies. OBJECTIVE Before introducing a new device or a passive data collection approach, it is important to determine what will be culturally acceptable and feasible. The objective of this study was to develop a systematic approach to determine acceptability and perceived utility of potential passive data collection technologies to inform selection and piloting of a device. To achieve this, we developed the Qualitative Cultural Assessment of Passive Data collection Technology (QualCAPDT). This approach is built upon structured elicitation tasks used in cultural anthropology. METHODS We piloted QualCAPDT using focus group discussions (FGDs), video demonstrations of simulated technology use, attribute rating with anchoring vignettes, and card ranking procedures. The procedure was used to select passive sensing technologies to evaluate child development and caregiver mental health in KwaZulu-Natal, South Africa, and Kathmandu, Nepal. Videos were produced in South Africa and Nepal to demonstrate the technologies and their potential local application. Structured elicitation tasks were administered in FGDs after showing the videos. Using QualCAPDT, we evaluated the following 5 technologies: home-based video recording, mobile device capture of audio, a wearable time-lapse camera attached to the child, proximity detection through a wearable passive Bluetooth beacon attached to the child, and an indoor environmental sensor measuring air quality. RESULTS In South Africa, 38 community health workers, health organization leaders, and caregivers participated in interviews and FGDs with structured elicitation tasks. We refined the procedure after South Africa to make the process more accessible for low-literacy populations in Nepal. In addition, the refined procedure reduced misconceptions about the tools being evaluated. In Nepal, 69 community health workers and caregivers participated in a refined QualCAPDT. In both countries, the child’s wearable time-lapse camera achieved many of the target attributes. Participants in Nepal also highly ranked a home-based environmental sensor and a proximity beacon worn by the child. CONCLUSIONS The QualCAPDT procedure can be used to identify community norms and preferences to facilitate the selection of potential passive data collection strategies and devices. QualCAPDT is an important first step before selecting devices and piloting passive data collection in a community. It is especially important for work with caregivers and young children for whom cultural beliefs and shared family environments strongly determine behavior and potential uptake of new technology.

scholarly journals What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment

2021 ◽  
Vol 9 ◽  
Author(s):  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Ashley Hagaman ◽  
Celia Islam ◽  
Ada Thapa ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Data Collection ◽  
Social Interactions ◽  
Social Environment ◽  
Mobile Phones ◽  
Qualitative Interviews ◽  
Young Mothers ◽  
The Social ◽  
Audio Data

Background: The social environment, comprised of 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 into the social environment while conducting a study of mental health among adolescent and young mothers in Nepal.Methods: We enrolled 23 adolescent and young 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 2 weeks from the mothers using an app to record 30 s of audio every 15 min from 4 A.M. to 9 P.M. Audio data were processed and classified using a pretrained model. Each classification category was accompanied by an estimated accuracy score. Manual validation of the machine-predicted speech and non-speech categories 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, infant babbling and laughter, and sounds from baby toys. Sounds characterizing negative stimuli included yelling, crying, screaming by adults and crying by infants. Sounds associated with social isolation included silence and TV or radio noises. Speech comprised 43% of all passively recorded audio clips (n = 7,725). Manual validation showed a 23% false positive rate and 62% false-negative rate for speech, demonstrating potential underestimation of speech exposure. Other common sounds were music and vehicular noises.Conclusions: Passively capturing audio has the potential to improve understanding of the social environment. However, a pre-trained model had the limited accuracy for identifying speech and lacked categories allowing distinction 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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