Mobile Technology for Adaptive Aging Proceedings of a Workshop (2020) / Chapter Skim
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3 Mobile and Sensor Technology as a Tool for Health Measurement, Management, and Research with Aging Populations - Elizabeth Murnane and Tanzeem Choudhury
3 Mobile and Sensor Technology as a Tool for Health Measurement, Management, and Research with Aging Populations - Elizabeth Murnane and Tanzeem Choudhury
Pages 41-66
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, with global health care expenditures anticipated to reach $47 trillion by 2030 (Bloom et al., 2018) , as prevalence continues to increase worldwide (Saranummi et al., 2013)
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Today's top risk factors for premature death all relate to lifestyle choices (diet, physical activity, smoking, and excessive alcohol consumption) (Mensah, 2006)
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The ability to observe behavior continuously and in context also makes it possible to tailor interventions to optimize effectiveness for an individual user, plus these technologies provide an interface through which such feedback can be delivered. Adoption and Acceptability of mHealth Tools by Older Adults Recent years have seen a swell in personal technology penetration, especially mobile phones.
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, especially tools to monitor and manage symptoms, encourage physical activity, and remind of appointments ( limova, K 2016)
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Today, mHealth research on applying this style of in situ reporting to aging contexts has largely focused on the smartphone, given both its biquity as well as its support for rich interactions. Typically targeted indiu cators include physical activity (Maher et al., 2018)
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. Much existing work on mobile sensing for older populations has ocused on passively tracking mobility -- for example, using f ccelerometer and GPS data to assess physical activity and frailty (Castro a et al., 2015)
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. Rather than utilizing hardware sensors, "soft sensing" captures data from software usage logs to passively infer health indicators (De Choudhury, 2014)
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. More recently, others have placed sensors to automatically collect health metrics into furniture like chairs (Griffiths et al., 2014)
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However, compared to the aforementioned work to develop mHealthbased data collection and health assessment techniques, the research on the informatics and interventions side of the equation is more limited for aging groups. As mentioned, most interfaces focus on delivering textbased reminders and nudges (e.g., to take medication, complete condition- specific tasks, or perform general physical activity)
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In the aging context, pursuing more personalized and adaptive solutions is likely worthwhile, given the variety in older adults' expressed preferences regarding health topics to track (Davidson and Jensen 2013) , together with the fact that "older adults" can actually span multiple decades in age and may have therefore experienced highly variable historical contexts, life circumstances, and health trajectories.
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aims to organize a community around developing a standard for mobile health data. Important to note, however, is that these open platforms have been developed for general purpose use, which motivates research to investigate and take steps to extend their accuracy, coverage, and overall appropriateness when used by older populations and applied to adaptive aging contexts.
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. This work is motivated by the idea that people want to use mHealth technologies to answer specific questions like these about their health, but current tools fail to effectively support such diagnostic selftracking (Karkar et al., 2015)
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CONCLUSION Realizing the Potential of mHealth for Adaptive Aging mHealth technologies have the potential to play a positive, perhaps transformative, role in supporting the health and well-being of our aging population. To fully realize this potential, however, some barriers must be overcome and facilitating steps taken, including to both address general challenges as well as develop age-specific design solutions.
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-- and toward more qualitative representations of personal data and health feedback. For example, work on designing for populations with compromised concentration or other perception difficulties has developed novel informatics approaches that encode personal data (e.g., activity levels, hours slept, social interactions)
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More rigorous examinations are necessary to establish the efficacy of mHealth approaches in adaptive aging contexts. Further, existing mHealth systems are often one-off applications rather than extensible platforms, and implementation is needed of
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Similarly, mHealth strategies for large-scale measurement can help surface systematic health inequities, for example, by using accelerometry data from smartphones to reveal physical activity disparities in different cities around the world (Althoff et al., 2017)
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. Behavioral Data Gathering for Assessing Functional Status and Health in Older Adults Using Mobile Phones.
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. Design Requirements for Tech nologies That Encourage Physical Activity.
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. Empowering the Aging with Mobile Health: A mHealth Framework for Supporting Sustainable Healthy Lifestyle Behavior.
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. Effects of Three Motivationally Targeted Mobile Device Applications on Initial Physical Activity and Sedentary Behavior Change in Midlife and Older Adults.
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. Fish'n'Steps: Encour aging Physical Activity with an Interactive Computer Game.
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. Ecological Momentary Assessment Is a Feasible and Valid Methodological Tool to Measure Older Adults' Physical Activity and Sedentary Behavior.
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. What Is eHealth: A Systematic Review of P ublished Definitions.
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Pervasive Computing Technologies for Healthcare. Rawassizadeh, R., B.A.
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. Age-Related Difference in the Use of Mobile Phones.
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