Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Trifan, Alina; Oliveira, Maryse

doi:10.2196/12649

Cited by 110 publications

(98 citation statements)

References 135 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been a growing literature on using smartphone accelerometer or gyroscope data to perform activity recognition (see Straczkiewicz and Onnela [ 17 ] and Trifan et al [ 18 ] for systematic reviews on this topic). A common approach is to first extract features from the data (such as the mean and standard deviation of the acceleration data along a certain axis) and then use those features as covariates in a classifier.…”

Section: Introductionmentioning

confidence: 99%

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data

Huang

Onnela

2020

Sensors

View full text Add to dashboard Cite

Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.

show abstract

Section: Introductionmentioning

confidence: 99%

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data

Huang

Onnela

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Smartphones are the most ubiquitous of passive sensors ( 91 ). Already, 96% of Americans own a cellphone of some type, 81% have a smartphone, and even 71% of those with incomes < $30,000/year have a smartphone ( 92 ).…”

Section: Resultsmentioning

confidence: 99%

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

Sandifer

Knapp

Lichtveld

et al. 2020

Front. Public Health

View full text Add to dashboard Cite

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental Sandifer et al. Community Health Observing System observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

show abstract

“…Additional validation tools in combination with passive audio data can provide important insight into mothers' lives especially under domains like mother-child interaction and domestic violence, which are di cult to assess, especially in low-and middle-income settings (48,56). Studies focused on passive sensing data often use additional validation measures to ensure accuracy (13). We suggest similar approach to studying domains such as interaction and support.…”

Section: Discussionmentioning

confidence: 99%

“…Passive sensing data is increasingly being used in mental health research (13,14). It refers to the capture of users' information without their active input while they go about their daily lives (15,16).…”

Section: Introductionmentioning

confidence: 99%

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

Poudyal¹,

Heerden

Hagaman

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations

Cited by 110 publications

References 135 publications

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data

Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

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

Contact Info

Product

Resources

About