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

doi:10.21203/rs.3.rs-22755/v1

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Research article

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

https://doi.org/10.21203/rs.3.rs-22755/v1

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published 07 Apr, 2021

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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

Medical Informatics

Adolescent

Depression

Developing Countries

Mental Health

Postpartum Depression

Psychotherapy

Digital Health

Nepal

Child Health

Passive sensing on mobile devices refers to the capture of information that does not require users’ active input while they go about their daily lives [1, 2]. For example, accelerometers on smartphones can detect activities such as walking, riding in a vehicle, and standing. The Global Positioning System (GPS) captures location. Passive sensors also provide information on the number of steps taken in a day, heart rate variability, exposure to light and sound, and proximity to others with mobile devices. Passive sensing data provides a window onto experiences, behavior, and environments of individuals, all of which are important to understanding mental health and mental illness. Because the field of mental health lacks objective markers of disease such as viral loads, pathogen detection, and point-of-care testing for disease status, passive sensing provides a unique objective reference for mental health status [3, 4].

Passive sensing data collection can be especially helpful for health initiatives in low- and middle-income counties (LMIC), which are characterized by limited access to specialty health services and where some populations have low literacy [5]. Combined with effective interventions, passive sensing data collection has the potential to address major public mental health issues in LMIC. One mental illness of high prevalence and societal impact is postpartum depression, particularly because young mothers are often not identified or treated in LMIC. The prevalence of postpartum depression in LMIC ranges from 3% to 32% [6]. Passive sensing can help to better understand maternal mental health by recording physical activity, location, sleep, mother-child interaction, and the auditory environment. In a high-income country study of women with elevated perinatal depression symptoms, radius travelled as measured with GPS was associated with depressed mood. Women who traveled larger radii had milder depression than the women with severe depression who had smaller travel radii [7]. Given that maternal mental illness is associated with disrupted sleep, lack of social engagement, lack of stability in daily schedules, and altered interaction patterns with their infants [8–10], there is a host of opportunities to apply passive sensing data collection to improving diagnosis, monitoring, and treatment for mothers with depression.

Before designing a passive sensing-informed intervention program, it is crucial to understand the feasibility of collecting data [11] and cultural factors influencing acceptability [12]. Mobile technology for health (mHealth) use in the real world is impacted by technical challenges, familiarity with technologies, and cultural attitudes and practices [13]. Therefore, in this study, we explore the feasibility, acceptability, and perceived utility of collecting passive sensing data among depressed and non-depressed adolescent mothers in rural Nepal.

Setting

This study was conducted in Chitwan, a southern region of Nepal. The total population of Chitwan is 579,984. There is a diverse mix of castes and ethnicities in the area, with several different languages spoken. Chitwan has slightly better health and development indicators than the national average. The under 5 mortality rates for Chitwan is 38.6 per 1000 (national average is 52.9); it also has a higher literacy rate of 78.9% compared to the national average of 67% [14].Chitwan district was selected because of a longstanding established partnership with the local health system and a district-wide scaling-up of community-based mental health services that was being conducted [15]. Health posts were selected to maximize access to a large number of postnatal mothers and ensure available resources for mental health treatment. This study covered seven health facilities in rural areas of Chitwan. Recruitment and data collection occurred between November 2018 through April 2019.

In the research site, a psychological intervention was being delivered to support maternal mental health. The intervention, the Healthy Activity Program (HAP), is an evidence-based psychological treatment originally developed in India for delivery by lay counselors [16]. HAP has shown promise in reducing the symptoms of depression in Nepal [15]. However, in HAP, counselors rely on self-report to understand a client’s behavioral patterns between sessions. Passive sensing data collection if integrated into HAP has the potential to provide counselors with information related to the activity patterns, sleep, social interactions, and mood of the mothers. Incorporating passive data into HAP can aid counselors to provide personalized treatment. Our ultimate goal—after establishing feasibility and acceptability of passive sensing data collection as described in the current study—is to use passive sensing data collection to inform HAP delivery in Nepal [17].

Study Population and Sampling

Study participants were recruited at health posts during infant immunization camps. Camps were typically attended by 136 mothers on average every month. Inclusion criteria were mothers between 15–25 years of age with an infant aged between 1- 12 months living in the study area, and willingness to be screened for postnatal depression. Mothers were screened for depression using the Nepali Patient Health Questionnaire (PHQ–9), validated for the local population [18]. Mothers scoring below 7 were classified as ‘non-depressed,’ and those above 9 as ‘depressed’. Among Nepali adults presenting to outpatient services, a cut-off of 9 has a sensitivity of 94% and specificity of 69%, positive predictive value (PPV) of 0.33 and negative predictive value (NPV) of 0.99. A cut-off of 7 has a sensitivity of 1.00, specificity of 0.55, PPV of 0.26, and NPV of 1.00. Of note, psychometric values specific for Nepali postpartum mothers aged 15–25 are not available. Following the mother’s consent, a study team member arranged to visit her home for family consent. In case of participants under the age of 18, written informed consent was taken from an adult family member in addition to written assent from the participants. The study was approved by the Nepal Health Research Council (#327/2018) and George Washington University’s Institutional Review Board (#051845).

Study Procedure

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. Mothers were given a low-cost android Samsung J2 Ace smartphone and a passive Bluetooth beacon to be attached to her infant’s clothing. A female research assistant briefed each mother and her family on the technical use of the phone and beacon. The research assistant visited the mother’s home on average 5 times including for study briefing and consent, day one of data collection for technology delivery and training, day three of data collection for technology troubleshooting, and then weekly, with intermittent phone calls to additionally troubleshoot and provide any needed technology support. Mothers were instructed to keep their mobile phones with them as much as possible and attach the beacon to their infant’s clothing throughout the day. Mothers were asked to turn the mobile phones off and remove the beacon from the child during the night. Research assistants conducted the qualitative interviews at the end of the study duration (study day #14). Qualitative interviews were audio-taped, transcribed, and translated before coding and analysis. The interviews were first transcribed verbatim in Nepali and then translated to English by a bilingual translator. We followed a standardized Nepali mental health glossary for translation of emotional and psychological terms into English [22].

Technology and passive sensor data collection

The devices used in this study were selected following extensive ethnographic inquiry regarding acceptability and feasibility in the study site [12]. Two devices were considered culturally appropriate - a smartphone and a Bluetooth beacon. The Samsung J2 Ace smartphone is a cost-effective mobile phone (US $160) that is popular in the study setting. Most common, low-end mobile phones in Nepal cost US $70-$120, and therefore the device selected in the study was slightly more expensive than commonly-used devices. In the study area, most individuals owned mobile phones or have family members who own mobile phones. Hence, there is less risk of stigmatization because of phone use in the study. We selected the Samsung J2 Ace phone because it is widely available for purchase within Nepal and it was the cheapest option that could effectively run all the features and apps required for the study. The passive Bluetooth beacon was the RadBeacon dot ($10–15) developed by Radius Networks [19]. For a subset of mothers, we also piloted the use of smartwatches in place of the smartphone; models included Zeblaze Thor 4 Android smartwatch and Lemfo Lem8 Android smartwatch. The smartwatches were used to assess if a wrist worn device may provide the same data in a more convenient device.

The mothers were provided with the phone and the beacon for the duration of the study. They returned the devices after completing data collection. The smart devices collected 4 types of data—proximity, episodic audio, physical activity, and geographic location. To collect these data, we installed our custom-built Electronic Behavior Monitoring app (EBM version 2.0). The EBM app passively collected data for 30 seconds every 15 min between 4AM and 9PM. A folder, NAMASTE, was created automatically once the EBM app was downloaded on the smartphone. All data were stored in the folder. Details of each passive sensing domain are provided below:

Proximity of mother to infant: The proximity sensor (passive Bluetooth beacon) was fitted to the infant’s clothing, and the mother was asked to carry the mobile phone to record when the mother was in proximity to the infant. Every 15 minutes, the EBM app scanned for the presence of advertising packets from the assigned Bluetooth beacon and recorded whether the beacon was present or not.
Episodic audio recording: For episodic audio recording conducted every 15 minutes, the microphone in the phone was used to record 30-second audio clips saved in an MP3 format. The audio data were saved in a local folder in a mobile and processed for conversion to a wave form image using machine learning.
Physical activity: Activity recognition was used to record the predicted activity type (e.g., walking, standing still, cycling, riding a vehicle) at the time of audio recording based on the mobile phone’s accelerometer data.
Geographic location: GPS on the mobile phone collected the mother’s position each time the phone had an activity event (phone unlocking, Bluetooth scanning etc.).

We had previously produced a video to explain these data collection processes to potential study participants [12]. In addition, confidentiality management, such as deleting audio files was piloted with mothers using similar devices in South Africa [20]. On the devices, mothers can delete audio files before research assistants uploaded the data from the phones. Mothers were also instructed to turn off their phone anytime they choose in order to stop data collection.

Qualitative data collection

To assess feasibility and acceptability of passive data collection, we triangulated several sources of data including in-depth interviews (IDIs) performed at the end of 14 days of passive sensing data collection, field notes recorded by research assistants from each participant encounter, and memos documenting the significant events related to experience of technology use documented by the a project lead based in the Chitwan study site. Female research assistants conducted IDIs using a semi-structured interview guide lasting between 20–45 minutes. Questions elicited maternal experiences and perceptions of the technology and EBM application, covering feasibility, social acceptability, confidentiality, utility, and recommendations for improvement. Importantly, the research assistant had established meaningful rapport with both the mother and her family (on average visiting the mother’s home 3–5 times), permitting more comfort and allowing detailed and frequent field notes to capture examples and texture not captured by the IDI. The Consolidate Criteria for Reporting Qualitative Studies (COREQ) checklist is included in the Supplemental Online file [21].

Data analysis

Passive sensing data analysis

Raw data retrieved from the sensors using the EBM v2.0 app was saved as comma separated files (csv) on the device with one file being produced per day. The time stamped data were then extracted from these csv files before being processed and loaded into a SQL database. The same approach was followed for each of the study devices resulting in a single aggregated data set per sensor, i.e. proximity, activity, audio, and GPS. During this process raw proximity data were turned into an hourly binary reading indicating the presence or absence of a beacon. Raw activity data were trimmed from the full list of activities detected along with their probability scores to only the activity with the highest associated probability. The 30 second audio wav files were processed by our machine learning model and the audio classification label with the highest probability score saved. Finally, GPS data was also dichotomized indicating either the presence or absence of a GPS reading with an accuracy reading of less than 50 meters.

These four datasets were then loaded into Python where they were merged into a single data frame with a datetime index. Whenever a sensor had no hourly reading it was coded as missing. This data frame was then cleaned to remove data of mothers who dropped out of the study, and fix outliers introduced by some readings missing a valid datetime value. Standard exploratory data analysis was performed on this data frame including the calculation of measures of central tendency, range and missing value analysis. Plots and tables were produced on the full dataset (4AM to 9PM), a dataset trimmed to include only values between 7AM and 7PM and finally a dataset trimmed to include only values between 6AM and 9PM.

Qualitative data analysis

In our prior formative study, we categorized feasibility and acceptability of wearable technology for passive sensing data collection for health research in LMIC into 6 domains [12]. We incorporated these for the current study, exploring domains relevant to our data including: technical issues, interference, confidentiality, safety, utility, and communication (Table 1)..

Table 1

Domains of the Inquiry
*Domains*	*Definition*	*Code Examples*
Domain 1: Technical issues	The degree to which technical issues in the devices impact use or the devices.	mobile use, data usage, family involvement
Domain 2: Interference	The degree to which the device may impact physical functioning, activities, or daily routines.	mobile use, battery, beacon use
Domain 3: Confidentiality	The degree to which the device would protect personal information.	privacy concerns, social perspective
Domain 4: Safety	Perceptions regarding health risks or put a child, mother or family at the risk of mugging or theft. This domain also explores safety concerns mothers have over losing or breaking the device itself.	child safety, mother safety, device safety
Domain 5: Utility	The perceived benefits of the device for improving caregiver and child health, development, and mental health.	non-study specific utility, study specific utility, misperceptions and other perceptions
Domain 6: Communication	Communicating study objectives and device use to the mothers during the consent process at the beginning of the study and by study team engagement throughout the study duration.	autonomy, study team engagement, consent/debriefing process

Interview transcripts were combined with their relevant field notes, providing additional context. Three researchers (AH, AP, SM) independently read two transcripts each to generate common themes. SM generated a preliminary codebook. AH, AP, and SM then modified and iterated the codebook during the process of obtaining intercoder agreement. Intercoder agreement was assessed between all coders on a portion of coded data until a kappa of 0.76 was achieved. Three researchers (SM, AT, CI) coded the interview transcripts in NVivo 12 [23]. Code summaries were produced for each of the sub-domains following an applied thematic approach [24, 25]. Summaries were discussed, enhanced, and revised with several authors (SM, AT, AP, AH) to ensure depth, breadth, and systematic comparisons across informants. Case memos were independently reviewed and analyzed by SM and AT to identify case studies relevant to the domains of the paper.

Recruitment and Sample

We screened 782 mothers, of whom 320 were eligible for inclusion based on age criteria of the mothers and infants. We consented the eligible mothers and screened them for depression. Approximately 92% of the eligible mothers scored below the depression cut-off (n = 294) and 8% scored above the cut-off (n = 26). We serially enrolled non-depressed mothers with an initial target of 25 mothers. Approximately two-thirds of the non-depressed mothers and their families provided consent and were enrolled, with a final sample of 27 non-depressed mothers. All mothers subsequently screened who scored below the depression cut-off were not eligible for the study. Of the 26 mothers who screened positive for depression, 11 of the mothers and their families provided consent and enrolled in the study. Table 2 contains demographic information of the participants. Case memos revealed reasons for non-participation of both non-depressed and depressed mothers, and memos were also used to document reasons for withdrawing from the study. Reasons for not participating included mothers moving outside of the study area, inability of the research team to contact mothers following initial screening in the health facilities, families not-consenting to participate in the study, and mothers too busy to participate due to family obligations. Two participants withdrew from the study before completing the full 2 weeks of passive sensing data collection (additional details on why they withdrew are provided below).

Table 2

Demographic characteristics of study sample
Total participants	N (%)
All	38 (100)
Depressed	11 (28.95)
Non-depressed	27 (71.05)
Mother age	N (%)
15-18	11 (28.95)
19-22	22 (57.89)
23-25	5 (13.16)
Caste	N (%)
Brahmin	4 (10.53)
Chhetri	4 (10.53)
Janajati	18 (47.37)
Dalit	12 (31.58)
Religion	N (%)
Hindu	32 (84.21)
Buddhist	3 (7.89)
Christian	3 (7.89)
Education	N (%)
Grades 1-5	7 (18.42)
Grades 6-10	24 (63.16)
Grades 11-12	7 (18.42)
Child gender	N (%)
Male	16 (42.11)
Female	22 (57.89)
Child age	N (%)
1 to 4 months	21 (55.26)
5 to 8 months	10 (26.32)
9 to 12 months	7 (18.42)
Total number of children	N (%)
Less than equal to 1	31 (81.57%)
More than 1	7 (18.43%)
Participant Occupation	N (%)
Business	3 (7.89)
Housewife	29 (76.32)
Agriculture	4 (10.53)
Day wage laborer	2 (5.26)
Electricity	N (%)
Yes	36 (94.74)
No	2 (5.26)
Wifi internet at home	N (%)
Yes	3 (7.89)
No	35 (92.11)
Smartphone ownership	N (%)
Yes	26 (68.42)
No	12 (31.58)

Passive Sensing Data collection

We collected activity, audio, GPS and infant proximity data through passive sensor readings (every 15 minutes from 4am to 9pm, daily). Table 3 provides the total passive data collected between 4 AM - 9 PM among depressed, non-depressed, and the total sample. Audio and activity data were captured more often than GPS and proximity data on average. For the 38 young mothers, the cumulative maximum number of passive sensor readings (every 15 minutes from 4am to 9pm, daily) was 9,605, see Table 3. Per participants, there were, on average, 57.4% of audio, 50.6% of activity, 41.1% of proximity, and 35.4% of GPS data readings.

Table 3

Total passive data collected daily from 4 AM to 9 PM
*Passive sensing domain*	*Total possible number of readings*	*Observed number of readings*	*Percent mean observed readings per participant*	*Range of percent readings (min—max)*	*Median percent readings (IQR)*
All participants (n=38)
Audio	9,605	5579	57.4%	11.7- 97.2%	62.6% (25.1)
Activity	9,605	5001	50.6%	0-95.5%	63.2% (51.1)
Proximity	9,605	4168	41.1%	0-84.2%	47.6% (32.5)
GPS	9,605	3482	35.4%	0-85.3%	39.2% (45.4)
Non-depressed participants (n=27)
Audio	6,902	3,926	57.8	13.2-87.9	63.7 (17.5)
Activity	6,902	3,304	48.1	0-87.9	62.4 (53)
Proximity	6,902	3,087	43.9	1-84.2	51.8 (35.4)
GPS	6,902	2,527	36.7	0-85.3	40.8 (47.9)
Depressed participants (n=11)
Audio	2,703	1,653	56.7	11.8-97.1	51.2 (31.9)
Activity	2,703	1,697	56.9	0-95.5	64.0 (28.1)
Proximity	2,703	1,081	34.0	0-67	32.9 (20.8)
GPS	2,703	955	32.4	0-77.5	30.4 (39.9)

Figure 1 shows the average passive data readings by the time of the day. Data collection was lower than the total possible readings in the early morning across all sensors and tapered off at night, but was generally consistent from 10 AM to 6 PM. We explored possible explanations for differences in successful data collection by time of day and sensor type along with description of qualitative results to illuminate these differences. We categorize these findings below based on the 6 qualitative domains for feasibility and acceptability.

Domain 1: Technical Feasibility

Mobile phone battery charge, data usage, and positive and negative family involvement were the main technical feasibility issues identified that limited data collection throughout the day. Passive sensing requires the phone to be turned on. Morning data (and some evening data) were most likely to be missing because mothers typically were instructed to turn their phones off at night before bed and then turn it back on when awakening in the morning. Subsequently, there were gaps in data collection in the evening and early morning hours.

“I used to switch off this watch at night and connect to the charger and switch on in the morning. … In the daytime I used to switch off this watch again and connect it to the charger.”

(19-year old depressed mother)

Common feasibility challenges across all sensors that likely reduced data capture were lack of electricity, mothers forgetting to charge the phone, devices not retaining charge (especially smart watches), technical difficulties such as phones not connecting to the charger properly, or environmental factors such as protecting the device from rain. In general, there were more technical issues reported by mothers who used smartwatches with frequent battery draining. We tested the use of smartwatches instead of mobile phones with three participants but 0% of the activity data was collected due to the smartwatch not supporting activity data collection (see Text Box 1).. The use of smartwatches with no activity data contributed to reducing the average across all participants to 60% at best, with the greatest data capture during midday.

Text Box 1: Limitations of passive sensing data collection with a smartwatch

For a 21-year-old non-depressed mother with a 4-month old infant, it was her first experience of using a smartwatch. She was excited and curious about the device and her husband also liked the idea of her using it. She did not have a problem attaching the beacon on the child’s clothing. However, she felt uncomfortable wearing the watch on her hand. She often had to get her hands wet, such as when washing clothes and doing dishes. She was concerned about the possible damage that could be caused by the water. She got worried about it while using it. She eventually completed the study with her husband’s support who helped her charge the smartwatch. We collected 62.5% and 51.9% audio and proximity data but were unable to collect any GPS or activity data due to technical issues with the smartwatch.

Of the total possible readings, about 50% of the GPS data was collected during the high period of data capture from midmorning to early evening (10 AM - 6 PM, see online Supplemental File).. A likely reason for less successful data capture compared to audio was that GPS data collection was not triggered the same way as audio, proximity, or activity data were. The GPS data collection required WiFi or 3G signal for collection. Therefore, data collection was low because mothers were mostly indoors which limited this GPS connection. Initially, we used a combined method that included both mobile data and direct GPS connection to collect GPS location of the mothers. However, mothers and their families quickly used available data, typically by watching YouTube videos, which then led to not enough prepaid data for GPS collection. We switched to direct connection to the satellite to address the loss of data due to low mobile data by turning on high accuracy in the GPS settings.

Domain 2: Interference

We explored daily interference related to mobile phone and Bluetooth beacon use. Consistent daily mobile phone use was acceptable to most mothers. However, carrying two mobile phones (one for the study and one personal phone) was considered interfering with daily activities. Additionally, mothers shared their concern about the probability of losing mobile phones if they had to carry two phones at all times. Mothers working in a shop or office outside the home and mothers who were farming found it even more challenging to carry two mobile phones. Additionally, it was difficult for the study team to contact mothers who worked during the day if she had technical issues with the devices, which caused further disruption in data collection [Text Box 2]..

Text Box 2: Working mothers and passive data collection

A 23-year old non-depressed mother with a 10-month old infant was a small business owner with a shop in which she made toy dolls and trained others in this trade. She received assistance from her family to use the smartwatch. Her husband helped her to charge the smartwatch and reminded her to put the beacon on the child. As she was busy with work, it was difficult for the team to get time for regular visits. So, she suggested recruiting housewives instead of employed mothers so that they could dedicate their time to the study. She completed the study, and we were able to collect 62.7% of audio and 54.11% of proximity data. (No GPS or activity data were recorded due to technical issues in the smartwatch.)

The participants who did not find mobile phones interfering usually had clothes with pockets or used the mobile phones for non-study related activities such as watching videos or listening to music. Bluetooth beacons presented more challenges with daily interference. This may have been due to beacon novelty to the mothers so they felt more concerned initially about the device than about mobile phones. One of the mother’s major concerns was physical discomfort to the baby. Mothers generally put the beacons on the baby and took it off at night, or during oil massages and baths. Some mothers found the routine of putting beacons on the baby tedious after the first few days with some mothers delaying or forgetting to put the beacon on the baby after bathing or oil massages.

The proximity data was consistently lower than both audio and activity data with about 60% data collection at midday, even though it was triggered at the same time as audio and activity. One possible reason could be the participants turned off the Bluetooth signal on their mobile phones.

Domain 3: Confidentiality

We collected about 70–80% of audio data during the day (10 AM—6 PM, see Supplemental Online File).. Audio recordings were the data type most concerning to the participants from a confidentiality perspective. At least 11 mothers in our study expressed concerns of being audio recorded. One of the reasons was fear that study staff would listen to the audio clips and share private information with other people in the community, or that the community would know about the family disputes. For instance, two participants reported:

“If I talk about difficulty, mostly I am worried that other people will listen to all our discussions. They will know all our family problems. And they will talk about our problems everywhere.”

(24-year old depressed mother)

Interviewer [I]: Do you like this beacon and this mobile? Do you like to use this mobile and fix this beacon on your baby?

Participant [P]: I like using the devices but I feel worried because you might know all our family matters and our conversations.

I: We don’t listen to those recordings.

P: You will not share those recordings with other people?

- (21-year old depressed mother)

Some participants reported changing their behavior such as spending more time with the baby, talking lovingly or softly around the baby, shutting the mobile off during family arguments, or asking family members to not use bad words. Each of these behaviors may have reduced the audio capture. Mothers with alcoholic family members and mothers in conflict with their mothers-in-law usually showed the greatest privacy concerns. Family members of such households also asked mothers to switch their mobile phones off when they were under the influence of alcohol, or delete audio recordings that had their voices (See Textbox 3)..

“We have different conditions in our home. Sometimes people quarrel and we have arguments in our house. This mobile might record all those things so I have to switch off this mobile.”

- (17-year old non-depressed mother)

P: [Smiling] This mobile records sound. And all the recordings were stored in its memory so my husband told me to delete his recordings.

I: When did you switch off this mobile at night?

P: Sometimes at 6 or sometimes at 7 pm.

I: And what about your sisters and your own mother?

P: Sometimes when I start talking about my mother-in-law, my mother tells me not to talk about her, this mobile might record the voice and save it.

(16-year old non-depressed mother)

Text Box 3: Deletion of data and other reasons for low data capture

The family of a 21-year old depressed mother were squatters living near the jungle. They were attacked by wild animals and forced to move to a public land where they have been living temporarily. They lost their livestock and were afraid for their lives. They did not have access to the basic facilities like water tap, toilet, and electricity. When they were approached by the research assistants for the study, they agreed to participate but due to lack of electricity they charged their mobile at a neighbor’s house. They also used the mobile as a torch light during night time. The participant’s husband was unemployed. The participant had two kids to take care of, and she was very concerned for their future. Apart from the financial problem, the family had alcohol and smoking problems too. The husband used to smoke all the time whereas the in-laws regularly drank alcohol.

The participant was concerned about the device safety and money to pay for it if anything happens to the device. She was provided with a power bank to charge the mobile and assured not to worry if something happened to the devices. The provision of a power bank helped to keep the mobile running for a longer period of time. The participant no longer had to worry about leaving her mobile at the neighbor’s house and fearing that it could be stolen when she was not there.

We collected 73.9% of activity, 41.6% of audio, 10.5% of GPS, and 29.0% of proximity data from the mother. The low GPS data collection was a result of excessive data usage. During data collection, we relied only on mobile data to collect GPS. We later changed to direct connection to the satellite due to this and similar situations where mothers ran out of pre-paid data. There was lower audio and proximity data collection in comparison to activity data. In our qualitative interviews, the mother shared that her husband and mother-in-law listened to the audio files and deleted the ones that had their voices. Proximity data collection was interrupted when the Bluetooth was turned off on the phone, which was another reason for the low data collection.

Domain 4: Safety concerns

Three types of safety concerns were highlighted by participants: (1) safety of the infant when the Bluetooth beacon was attached to his/her clothing, (2) mother’s safety when using mobile phones, and (3) physical safety of the devices. Among the three, child safety was the most concerning to the mothers. One major child safety issue was physical discomfort that the beacon could cause to the infant, such as device poking the baby during sleep.

"I think that this beacon might poke my baby and make it difficult for him to sleep.”

-(21-year old non-depressed mother)

To avoid physical discomfort, mothers were instructed to remove the beacons when the baby was sleeping or mothers moved the device over multiple layers of clothes to avoid poking. The feeling of potential discomfort to the child hindered the consistent use of beacons and may have been one factor for reduced proximity data capture.

“While using these technologies, I thought this beacon might poke my baby and sometimes I removed the beacon.”

- (17-year old non-depressed mother)

Despite child safety concerns, there were two major facilitators that propelled mothers to continue using the device - trust in the study staff and no adverse effect to the baby after the first few days of use. Because the study staff reached out to the mothers in health posts where mothers went for regular checkups and immunization, they trusted the health workers in the health facility. The study staff coordinated with the health workers, and were therefore looked at by the mothers as trustworthy. Second, despite initial concern, when the baby did not get sick or have adverse effects in the first few days of use, the mothers were convinced that the beacons were safe:

I: Did you think that it might affect your baby or your baby might feel difficulty due to this beacon?

P: In the beginning I had those types of [negative] thoughts but after using [the devices] regularly for many days I didn’t have that type of thought anymore.

I: What types of thoughts did you have in the beginning?

P: That my baby might get sick, or it might have some health effects.

(23-year old non-depressed mother)

Mother’s safety was less of a concern in comparison to child’s safety. In general, all the mothers thought the mobile phones and beacons did not have an adverse effect on the mothers. A factor facilitating the use of smartphones was mother’s prior experience with mobile phones. Because mothers were familiar with smartphones, they did not think it would affect their health.

The final safety concern we explored was potential theft or breakage of the devices. Mothers, especially the ones from poor economic backgrounds, were scared that the study devices could get stolen or broken. Despite assurance from the study team that they were not liable in case of theft or accident, mothers were still anxious, especially for the first few days. The study team provided support and reassurance to the mothers during subsequent home visits to assuage remaining anxiety related to device safety.

Domain 5: Perceived utility

In general, mothers and families did agree to and continue using the phones throughout the period because of perceived benefits. Some of the perceived benefits aligned with the study goals, while some benefits were non-study related. Among the study-aligned perceived utility, mothers mentioned using the beacon and mobile phone to know the distance between them and their babies throughout the days. They could see the actual distance in smart devices. They also knew the mobile recorded their sounds, movement, and activities. Some mothers went back and listened to their audio clips. For perceived utility not related to the study purpose, mothers reported that they used the phone for listening to music, taking pictures and videos, using Facebook and watching YouTube videos. Perceived utility, however, varied across participants. For example, when asked about the utility of the beacons, some mothers said that the study showed how much they loved their babies. Other participants speculated that the data could help understand growth and brain development of the baby, or help in conflict resolution at home.

I: - I gave you this watch and fixed this beacon on your baby for two weeks. How was your experience these two weeks? What was your experience while using this watch and beacon? Please share something about that.

P: - I think I got a chance to provide more care to my baby. I got a chance to learn many things from the technologies that you provided me. This watch helps to find out whether we are speaking the truth or not. This watch records our voices continuously for a long time. One thought is continuously stuck in my heart-mind: through these recordings we can find out if someone is hiding something.

(21-year old non-depressed mother)

Some mothers thought the technology could record the time spent with the baby or identify their mood changes during the day.

I: - Do you know anything about why you are using this beacon and mobile? Though I had already told you about its use, what do you think about this technology?

P: - Yes, I know something. This is given to mothers between the age of 18 to 25 and their babies below 1 year. These technologies are for observing the changes in a mother like being irritated, distance between mother and the baby and problems in family relations. In the future, our daughter-in-law’s granddaughters would benefit from this technology. That’s why I agreed to use this technology.

(24-year old depressed mother)

Domain 6: Communication

Communication facilitated passive data collection through three subdomains - study team engagement, tech literacy, and autonomy of using devices. The importance of the study team engagement was critical when explaining the technology and addressing any queries that mothers had during the study duration. Mothers enjoyed their interactions with the study staff, especially when the study staff asked them about their children and family. They enjoyed study staff visiting them every few days to discuss any new queries and talk to the family members about the technology. We also provided the mothers with a study brief handout in Nepali, as a support tool, so that other family members and neighbors could read and understand about the study themselves during and after the consent process. The study tool supported mothers in answering family’s or neighbors’ questions about the study when the study team was not physically present to answer those questions.

One of the major social influences for acceptability was collaboration with local health posts for screening and recruitment at the community level. The recommendation from the health workers helped the study team to establish rapport with the participant and then follow up through the home visits. With the study team’s consistent technical support and clear communication of the study findings, the mothers felt more involved in the data collection process. They also felt more empowered to censor data collection by turning on/off the mobile device or beacon if needed. For example, 11 mothers described that they switched the phones off or left the phone in another room during family discussions, particularly to avoid recording any disputes or bad language.

Although 32% of study mothers were new to smartphone technology, all mothers confidently described their ability to navigate and operate the varying features by the second week. Mothers made decisions when or whether to attach the beacons on the child’s clothing as well. To support autonomy of using the devices, we found family engagement and consent to be important facilitators in both the research process and successful implementation of passive data sensing. Mothers felt more confident and comfortable when the study team explained the technology and study objectives to their families, especially to the family members in decision making roles such as husbands and mothers-in-law. As per our protocol, the study team visited the mother’s family after the initial screening at the vaccination clinics. Family consent helped the family understand the technology better and ask questions to the study team. Mothers generally said they were able to answer questions on the study objectives independently, but the family consent helped them get support from family members when the mothers had to explain the technology to non-family members.

Text Box 4: Other reasons for low data collection—religious concerns

No participants refused to participate because of religious beliefs with the exception of one family that was concerned that the technology was used for Christian religious conversion. In the study area, there are a large number of missionaries and concern in some communities about Hindu, Buddhist, and other religious practitioners being coerced into Christianity.

A 22-year old non-depressed motherwithdrew from the study after a few days. A research assistant visited the participant at her home and gave her the devices in presence of other family members after explaining the study to them. One after another, male family members turned up to inquire about the study. The husband was said to be away from home at that time. After a few days, upon his return, he called a research assistant asking her to come to take back the devices. The head of household said initially that they withdrew because they were not interested in this kind of study. But we later learned that the main reason they asked to mother to withdraw was that they suspected that the technological devices were being used to convert their religion to Christianity. We learned that the participant wanted to continue the study but was forced to drop out by her husband and father-in-law.

We collected 14.5% of activity, 65.5% of audio, 6.3% of GPS, and 7.5% of proximity data from the mother. This mother was also was one of the earliest participants we gave the devices to, as we were still making changes to the technology for appropriate data collection. The lower GPS data could be due to the mobile phone running out of data. Other data collection could have been affected by social factors. The mother’s family later told the study team that they were reluctant to use the devices, including the beacon on the child. The lower proximity data collection could mean that the Bluetooth on the mobile phone was switched off most of the times. The higher audio data indicates that the mobile phone was still switched on most of the times, although functionality such as Bluetooth was likely turned off.

Discussion

This study collected passive sensing data from 38 young mothers (27 non-depressed and 11 depressed) in rural Nepal. We found that approximately half to two-thirds of mothers approached provided individual and family consent. We collected activity (50.6%), audio (57.4%), GPS (35.4%), and proximity (41.1%) data from the mothers. Multiple studies have explored the potential of using passive sensing data in depression [3, 26], bipolar disorder [27], and schizophrenia [28] using GPS, audio, accelerometer, and Bluetooth devices, and have listed technological challenges and privacy issues as their major feasibility challenges [11]. In our study, positive and negative family engagement, perceived benefits of passive data collection, privacy issues, and technical limitations (such as battery and data usage) influenced the amount of passive data collected.

While the majority did not provide consent for logistical reasons, some families were unwilling to let the mothers participate, highlighting the need for careful community engaged approaches to mHealth programs. Of note, fewer mothers with depression consented and enrolled and this may have been because of difficulty or reluctance in regard to family consent because depressed mothers may have been more likely to be in families where a husband or other members abused alcohol or had other behaviors that were socially stigmatized or raised safety concerns. Previous studies have reported perceived stigma as a barrier to participation among participants with mental health conditions [29–31], which could be the case for the depressed participants in our study. Positive family involvement in the study, especially in case of severe mental health illness, has been recommended in a prior study [32].

The modest rates of initial inclusion raise concern about acceptability of the data collection and also draws attention to the perceived benefit of passive data collection. For a number of the mothers and their families, they were unclear about the purpose of data collection. This raises the opportunity to improve participation by providing more comprehensive explanations of how this technology will improve health services, especially when conducting exploratory studies using advanced technology in LMIC.

Once mothers consented, their main concern and that of their families appeared to be audio recording and potential confidentiality violations. Mothers used strategies such as turning off the phone, deleting data, or leaving the phone in a different room to address these confidentiality concerns. Going forward, having the audio recordings immediately converted to visual analogs on the smartphone or scrambling audio to reduce concerns about confidentiality violations is important [11, 33, 34].

Technical shortfalls in data collection (e.g., limitations in data, battery, etc.) could potentially be addressed by using reverse data billing in which the research organization or health organization is billed for data collection through a post-paid contract rather than needing to provide mothers repeatedly with charge cards for data and airtime. For example, the EBM app and other StandStrong apps could be billed to the organization while charges for calls, texts, YouTube, and other personal social use are billed to the mother. In addition, in the future installing the StandStrong suite on participant’s personal phones rather than giving an extra study phone could reduce the challenges related to carrying two phones and helps with the learning curve because mothers are already familiar with their own phones [35]. Use of one’s own personal smartphone is common for smartphone-based interventions in high income settings.

We piloted smartwatches as another approach to passively collect data. At this stage in smartwatch technology, we found that affordable watches had limited functionality compared to Android smartphones in the same price range. In the future, smartwatches may have increased functionality to allow for these devices to more feasibility collect the needed information compared to carrying around multiple phones [36, 37].

Ultimately, this study is an important first step to integration of passive sensing data collection into health services for psychological services and other health care. We found that approximately 50% of data collection was achieved. This may be sufficient to inform some health services, and we identified a number of technical issues to improve feasibility that would increase data capture. However, the most important contribution of this work is highlighting the importance of clear and transparent communication with both young mothers and their families about the purpose and process of passive data collection. It is vital to assure that use of such technology does not increase risk of harm for mothers in vulnerable situations. Going forward additional efforts should be made to educate families about how the technology works and to have them actively engaged in the process. One possible future strategy is to explore how husbands could be more involved in the process, such as by having them also participate with the EBM app installed on their phones. Given that postpartum depression often involves relationship stressors among parents and other relatives, male involvement could both improve acceptability of passive sensing data collection and enhance the outcomes of psychological interventions.

Limitations

This study has a number of limitations that should qualify generalizability of the findings. The data collection strategies were evolving over the course of the study. For example, early in the study, we tried introducing smartwatches and then abandoned this strategy because of both limitations in which sensors worked and fears of mothers (e.g., getting the watches wet). Similarly, we changed the GPS data collection strategy midway through the strategy to increase data capture. Research assistants modified how they explained the study to mothers and families as we gained experience of the collection process. The reported data-capture rates mix together low collection rates from the beginning of the study and higher rates toward the end of the study. The collection rates include three participants who withdrew a few days into the collection process. Our collection rates are conservative estimates of what could be achieved in any studies going forward. Also, the technological literacy and technology landscape are rapidly changing. There is increasing availability of cellular networks in rural Nepal and rural residents are becoming more familiar with smartphones. We anticipate fewer technological barriers to this type of mHealth initiative in the future. However, attending to the social dynamics and daily patterns of phone use will be crucial for successful implementation of mHealth and passive data-augmented interventions in the future.

This study identified a number of technological barriers and facilitators to comprehensive passive data collection in a rural area of Nepal. Most of these technological barriers can be addressed. More importantly, we identified concerns related to confidentiality and interpretation of the passive sensing data collection. Passive sensing data collection has the potential to transform psychological treatments and other mental health services. Just as glucose monitoring, remote blood pressure monitoring, and other remote approaches to assessing health in real-time, real-world situations, passive sensing data can provide an as yet untapped glimpse into real world behavior and environment. However, to scale use of passive sensing data collection, the approach needs to be feasible and culturally acceptable to potential participants in mHealth programs. Successful implementation of StandStrong and similar passive data collection initiatives will require addressing these concerns and fully involving family members in mHealth initiatives.

Ethics approval and consent to participate

Ethical approval was received from the Nepal Health Research Council (#327/2018) and George Washington University Institutional Review Board (#051845). We obtained written informed consent from the participants and verbal informed consent from adult members of their household.

Consent for publication

The participants provided written consent for the publication.

Availability of data and materials

Data will be made publicly available upon publication of the final study results.

Competing interests

Funding

The study was funded by the Bill and Melinda Gates Foundation (Grant #. OPP1189927, PI: B. A. Kohrt)

Authors’ contributions

SMM, AP, and BAK drafted the manuscript. SMM, AP, AD, and CI conducted the qualitative data analysis. AH supervised the qualitative data collection and analysis. AvH developed the EBM app. AvH and PB developed the StandStrong app. AvH conducted the quantitative data analysis. SMM supervised data collection and onsite study implementation. BAK, AvH, and AH conceptualized the study and study design. All authors revised the manuscript.

Acknowledgements

We would like to thank Doerte Bemme for her contributions to the manuscript. We would like to thank mothers and their families who participated in this study. We also like to thank the field study team (Aasha Mahato, Bhagwati Sapkota, Sabita Lohani, Bindu Aryal, Kendra Chaudhary, Bibek KC, Sirjana Panday) and colleagues at Transcultural Psychosocial Organization Nepal.

1.Insel TR: Digital Phenotyping: Technology for a New Science of Behavior. JAMA 2017, 318(13):1215–1216.

2.Campbell AT, Shane B. Eisenman, Nicholas D. Lane, Emiliano Miluzzo, Ronald A. Peterson, Hong Lu, Xiao Zheng, Mirco Musolesi, Kristóf Fodor, and Gahng-Seop Ahn: IEEE Internet Computing. The rise of people-centric sensing 2008, 12(4):12–21.

3.Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC: Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J Med Internet Res 2015, 17(7):e175.

4.Bhat A, Goud BR, Pradeep JR, Jayaram G, Radhakrishnan R, Srinivasan K: Can Mobile Health Improve Depression Treatment Access and Adherence Among Rural Indian Women? A Qualitative Study. Cult Med Psychiatry 2020.

5.Ruzek JI, Yeager CM: Internet and mobile technologies: addressing the mental health of trauma survivors in less resourced communities. Glob Ment Health (Camb) 2017, 4:e16.

6.Baron EC, Hanlon C, Mall S, Honikman S, Breuer E, Kathree T, Luitel NP, Nakku J, Lund C, Medhin G et al: Maternal mental health in primary care in five low- and middle-income countries: a situational analysis. BMC Health Serv Res 2016, 16:53.

7.Faherty LJ, Hantsoo L, Appleby D, Sammel MD, Bennett IM, Wiebe DJ: Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy. J Am Med Inform Assoc 2017, 24(4):746–753.

8.Krawczak EM, Minuzzi L, Hidalgo MP, Frey BN: Do changes in subjective sleep and biological rhythms predict worsening in postpartum depressive symptoms? A prospective study across the perinatal period. Arch Womens Ment Health 2016, 19(4):591–598.

9.Howell EA, Mora P, Leventhal H: Correlates of early postpartum depressive symptoms. Matern Child Health J 2006, 10(2):149–157.

10.Mata J, Thompson RJ, Jaeggi SM, Buschkuehl M, Jonides J, Gotlib IH: Walk on the bright side: physical activity and affect in major depressive disorder. J Abnorm Psychol 2012, 121(2):297–308.

11.Cornet VP, Holden RJ: Systematic review of smartphone-based passive sensing for health and wellbeing. J Biomed Inform 2018, 77:120–132.

12.Kohrt BA, Rai S, Vilakazi K, Thapa K, Bhardwaj A, van Heerden A: Procedures to Select Digital Sensing Technologies for Passive Data Collection With Children and Their Caregivers: Qualitative Cultural Assessment in South Africa and Nepal. JMIR Pediatr Parent 2019, 2(1):e12366.

13.Slater H, Campbell JM, Stinson JN, Burley MM, Briggs AM: End User and Implementer Experiences of mHealth Technologies for Noncommunicable Chronic Disease Management in Young Adults: Systematic Review. J Med Internet Res 2017, 19(12):e406.

14.United Nations Statistics Division: National Population and Housing Census 2011: National Report. In.; 2011.

15.Jordans MJD, Luitel NP, Garman E, Kohrt BA, Rathod SD, Shrestha P, Komproe IH, Lund C, Patel V: Effectiveness of psychological treatments for depression and alcohol use disorder delivered by community-based counsellors: two pragmatic randomised controlled trials within primary healthcare in Nepal. Br J Psychiatry 2019, 215(2):485–493.

16.Patel V, Weobong B, Weiss HA, Anand A, Bhat B, Katti B, Dimidjian S, Araya R, Hollon SD, King M et al: The Healthy Activity Program (HAP), a lay counsellor-delivered brief psychological treatment for severe depression, in primary care in India: a randomised controlled trial. Lancet 2017, 389(10065):176–185.

17.Poudyal A, van Heerden A, Hagaman A, Maharjan SM, Byanjankar P, Subba P, Kohrt BA: 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. JMIR Res Protoc 2019, 8(8):e14734.

18.Kohrt BA, Luitel NP, Acharya P, Jordans MJ: Detection of depression in low resource settings: validation of the Patient Health Questionnaire (PHQ–9) and cultural concepts of distress in Nepal. BMC Psychiatry 2016, 16:58.

19.RadBeacon Dot [https://store.radiusnetworks.com/products/radbeacon-dot]

20.van Heerden A, Wassenaar D, Essack Z, Vilakazi K, Kohrt BA: In-Home Passive Sensor Data Collection and Its Implications for Social Media Research: Perspectives of Community Women in Rural South Africa. J Empir Res Hum Res Ethics 2020, 15(1–2):97–107.

21.Tong A, Sainsbury P, Craig J: Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care 2007, 19(6):349–357.

22.Acharya B, Basnet M, Rimal P, Citrin D, Hirachan S, Swar S, Thapa P, Pandit J, Pokharel R, Kohrt B: Translating mental health diagnostic and symptom terminology to train health workers and engage patients in cross-cultural, non-English speaking populations. International Journal of Mental Health Systems 2017, 11(1):62.

23.QSR International: NVIVO qualitative data analysis software. In., 10 edn. Doncaster, Australia: QSR International Pty Ltd.; 2012.

24.Bernard HR: Analyzing Qualitative Data: Systematic Approaches, vol. 2nd: SAGE Publications Inc; 2016.

25.Guest GS: Applied thematic analysis, vol. 1st edition: SAGE Publications, Inc; 2011.

26.Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, Mohr DC: Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res 2011, 13(3):e55.

27.Beiwinkel T, Kindermann S, Maier A, Kerl C, Moock J, Barbian G, Rossler W: Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study. JMIR Ment Health 2016, 3(1):e2.

28.Ben-Zeev D, Wang R, Abdullah S, Brian R, Scherer EA, Mistler LA, Hauser M, Kane JM, Campbell A, Choudhury T: Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia. Psychiatr Serv 2016, 67(5):558–561.

29.Woodall A, Morgan C, Sloan C, Howard L: Barriers to participation in mental health research: are there specific gender, ethnicity and age related barriers? BMC Psychiatry 2010, 10:103.

30.Jorgensen R, Munk-Jorgensen P, Lysaker PH, Buck KD, Hansson L, Zoffmann V: Overcoming recruitment barriers revealed high readiness to participate and low dropout rate among people with schizophrenia in a randomized controlled trial testing the effect of a Guided Self-Determination intervention. BMC Psychiatry 2014, 14:28.

31.Kannisto KA, Korhonen J, Adams CE, Koivunen MH, Vahlberg T, Valimaki MA: Factors Associated With Dropout During Recruitment and Follow-Up Periods of a mHealth-Based Randomized Controlled Trial for Mobile.Net to Encourage Treatment Adherence for People With Serious Mental Health Problems. J Med Internet Res 2017, 19(2):e46.

32.Kumar D, Tully LM, Iosif AM, Zakskorn LN, Nye KE, Zia A, Niendam TA: A Mobile Health Platform for Clinical Monitoring in Early Psychosis: Implementation in Community-Based Outpatient Early Psychosis Care. JMIR Ment Health 2018, 5(1):e15.

33.Kotz D, Gunter CA, Kumar S, Weiner JP: Privacy and Security in Mobile Health: A Research Agenda. Computer (Long Beach Calif) 2016, 49(6):22–30.

34.Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T: Automatic detection of social rhythms in bipolar disorder. J Am Med Inform Assoc 2016, 23(3):538–543.

35.Ben-Zeev D, Schueller SM, Begale M, Duffecy J, Kane JM, Mohr DC: Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm Policy Ment Health 2015, 42(2):157–167.

36.Dewa LH, Lavelle M, Pickles K, Kalorkoti C, Jaques J, Pappa S, Aylin P: Young adults’ perceptions of using wearables, social media and other technologies to detect worsening mental health: A qualitative study. PLoS One 2019, 14(9):e0222655.

37.Arsand E, Muzny M, Bradway M, Muzik J, Hartvigsen G: Performance of the first combined smartwatch and smartphone diabetes diary application study. J Diabetes Sci Technol 2015, 9(3):556–563.

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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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Abstract

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Introduction

Methods

Setting

Study Population and Sampling

Study Procedure

Technology and passive sensor data collection

Qualitative data collection

Data analysis

Passive sensing data analysis

Qualitative data analysis

Results

Recruitment and Sample

Passive Sensing Data collection

Domain 1: Technical Feasibility

Domain 2: Interference

Domain 3: Confidentiality

Domain 4: Safety concerns

Domain 5: Perceived utility

Discussion

Limitations

Conclusion

Declarations

References

Supplementary Files

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