Browsing by Author "Maharjan SM"

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    Utilizing passive sensing data to provide personalized psychological care in low-resource settings.
    (F1000 Research Ltd, 2020) Byanjankar P; Poudyal A; Kohrt BA; Maharjan SM; Hagaman A; van Heerden A
    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.
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    What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment.
    (Frontiers Media S.A., 2021-03-29) Poudyal A; van Heerden A; Hagaman A; Islam C; Thapa A; Maharjan SM; Byanjankar P; Kohrt BA; Kyriakopoulos M
    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.