What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment.
What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment.
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DOI：
10.3389/fpubh.2021.633606
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发表时间：
2021
影响因子：
5.2
通讯作者：
Kohrt BA
中科院分区：
文献类型：
--
作者：
Poudyal A;van Heerden A;Hagaman A;Islam C;Thapa A;Maharjan SM;Byanjankar P;Kohrt BA
背景：由社会支持、社会负担和互动质量组成的社会环境影响包括心理健康在内的一系列健康结果。移动电话上的被动音频数据收集(例如，在不需要电话用户任何主动输入的情况下对听觉环境进行插曲记录)使了解社会环境的新机会成为可能。我们在尼泊尔进行了一项关于青少年和年轻母亲心理健康的研究，同时评估了手机上被动音频收集作为了解社会环境的窗口的使用情况。方法：我们招募了23名青少年和年轻母亲，她们首先参加了定性访谈，描述了她们的社会支持，并确定了与这种支持潜在相关的声音。然后，研究人员使用一款应用每15分钟录制30段S的音频，从凌晨4点到晚上9点，连续两周收集母亲们的片段录音。音频数据用预先训练好的模型进行处理和分类。每个分类类别都附有估计的准确度分数。对机器预测的语音和非语音类别进行了人工验证，以确保准确性。结果：在定性访谈中，母亲们描述了一系列积极和消极的社会互动以及伴随这些互动而来的声音。潜在的积极声音包括成人的讲话和笑声，婴儿的喋喋不休和笑声，以及婴儿玩具的声音。负面刺激的声音包括大喊大叫、哭泣、成年人的尖叫和婴儿的哭泣。与社会孤立相关的声音包括沉默和电视或广播噪音。语音占所有被动录制的音频片段(n=7,725)的43%。人工验证显示，语音的假阳性率为23%，假阴性率为62%，表明可能低估了语音暴露。其他常见的声音是音乐和车辆噪音。结论：被动捕捉音频有可能提高对社会环境的理解。然而，预先训练的模型在识别语音方面的准确性有限，并且缺乏区分积极和消极社会互动的类别。为了提高被动音频收集对了解社会环境的贡献，未来的工作应该提高音频分类的准确性，对声音星座进行编码，并将音频与其他智能手机的数据收集相结合，如位置和活动。
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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