With the progress of society and technology, people are paying more and more attention to sleep health issues. The APP are one of the methods widely used by people today. The design study of healthy sleep product APP has great significance today. Through analysis of the current state of sleep, user research, the functionality of hardware, and software sleep products. This research based on the user motivation, ability to use and environment of sleep products. Guiding and enabling user to perceive changes in their own sleep quality. Reducing visual distractions, enhancing user stickiness, improving perception and emotionality. According to the subjective and objective conditions of the use environment, a design method is proposed to balance the emphasis and weakness of visual design.

Xinwei Lan. Analysis of design requirements based on the components of healthy sleep APP products. Frontiers in Art Research (2022) Vol. 4, Issue 15: 67-70. https://doi.org/10.25236/FAR.2022.041513.

[1] Pan L, Li L, Peng H, et al. Association of depressive symptoms with marital status among the middle-aged and elderly in Rural China–Serial mediating effects of sleep time, pain and life satisfaction [J]. Journal of Affective Disorders, 2022, 303: 52-57.

[2] Fu J, Jiang B, Yang X. Intelligent hardware multimodal interaction design[C]//2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2016: 718-721.

[3] Ozkan-Yildirim S, Pancar T. Smart wearable technology for health tracking: What are the factors that affect their use? [M].//IoT in Healthcare and Ambient Assisted Living. Springer, Singapore, 2021: 165-199.

[4] Poongodi T, Krishnamurthi R, Indrakumari R, et al. Wearable devices and IoT[M]//A handbook of Internet of Things in biomedical and cyber physical system. Springer, Cham, 2020: 245-273.

[5] Chen M, Ma Y, Li Y, et al. Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems [J]. IEEE Communications Magazine, 2017, 55(1): 54-61.

[6] Aliverti A. Wearable technology: role in respiratory health and disease [J]. Breathe, 2017, 13(2): e27-e36.

[7] Lo’ai A T, Mehmood R, Benkhlifa E, et al. Mobile cloud computing model and big data analysis for healthcare applications [J]. IEEE Access, 2016, 4: 6171-6180.

[8] Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data[J]. Nature genetics, 2001, 29(4): 365-371.

[9] Ehrhardt-Martinez K, Donnelly K A, Laitner S. Advanced metering initiatives and residential feedback programs: a meta-review for household electricity-saving opportunities[C]. Washington, DC: American Council for an Energy-Efficient Economy, 2010.

[10] Nair H, Chintagunta P, Dubé J P. Empirical analysis of indirect network effects in the market for personal digital assistants [J]. Quantitative Marketing and Economics, 2004, 2(1): 23-58.

[11] Sacramento I, Wanick V. mHealth and the digital cyborg body: The running apps in a society of control [J]. Mobile e-Health, 2017: 39-70.

[12] Rabari C, Storper M. The digital skin of cities: urban theory and research in the age of the sensored and metered city, ubiquitous computing and big data [J]. Cambridge Journal of Regions, Economy and Society, 2015, 8(1): 27-42.