Mobile Technology for Adaptive Aging Proceedings of a Workshop (2020) / Chapter Skim
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2 Mobile Monitoring and Intervention (MMI) Technology for Adaptive Aging - Neil Charness, Walter R. Boot, and Nicholas Gray
2 Mobile Monitoring and Intervention (MMI) Technology for Adaptive Aging - Neil Charness, Walter R. Boot, and Nicholas Gray
Pages 21-40
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From page 21... ...
Although there are many commercial apps for smartphones aimed at supporting health, they have unknown efficacy and generally are not well designed for aging adults, failing to consider changing needs for the young-old, middle-old, and old-old age groups. We find that MMI technology for aging adults is in its infancy, with few good examples showing efficacy or cost effectiveness.
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This chapter ends with a discussion of how the RE-AIM framework can guide the development of MMI systems and closes by outlining potential research priorities. Sensor-based monitoring technology, both fixed and mobile, offers advantages and disadvantages for intervening to promote improved wellbeing for our aging population.
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for person, for proxy and network capability and availability/reliability and security unit of analysis may miss the person–family and person–community contexts for MMI (see the chapter by Fingerman et al.) , in line with the finding that caring for family members is a primary human social motivation (Ko et al., 2019)
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. Although older age had been associated with greater openness to adoption of health monitoring technology, when accounting for disability status, the effects of old age on openness are much smaller than those of disability status (Beach et al., 2009)
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is also a useful framework. One recent technology adoption model relevant to MMI is the smart wearable acceptance model (Li et al., 2019)
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Measurement Much of the literature concerning measurement capabilities of MMI technologies that we uncovered consists of feasibility pilot projects aimed at developing MMI technology systems. Many of these programs do not test such technologies with older adults, probably because of concerns with safety during simulated fall testing (studies that ask people to simulate the range of fall types)
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somewhat limits the scalability of the approach, because of the need to have a human in the loop to label/classify patterns. Intervention Behavioral research studies we reviewed that use mobile device data typically do not intervene based on building up behavioral prediction odels of study participants.
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The greater the number of chronic conditions, the greater the number of prescriptions (Buttorff et al., 2017) , possibly leading to complicated medication schedules, though medication adherence is sometimes better in older adults than middle-aged ones (Park et al., 1999)
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aimed at addressing nearly all aspects of health and disease, many with the goals of supporting MMI, including apps to help monitor and manage medication adherence, weight, nutrition, physical fitness, blood pressure, diabetes, sleep, and mood. Some apps track these activities and variables through self-report or sensors within the smartphone itself, while others rely on external sensors, including smartwatches, fitness trackers, telehealth devices, and web-cameras.
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. There is a long history of study of methods to improve adherence, for example, to health-related behaviors, and this has resulted in the publication of several systematic reviews.
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(Steventon et al., 2012) showed that a telehealth intervention for chronic conditions was not cost effective compared to usual treatment (Henderson et al., 2013)
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LIMITATIONS FOR USE OF MOBILE AND SENSOR TECHNOLOGY IN HEALTH Readiness in Aging Populations When designing a technological intervention, it is important to consider whether the target population is likely to have basic computer experience, or a home broadband connection. In early 2019, only an estimated 53 percent of older adults owned a smartphone (Pew Research Center, 2019)
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FUTURE DIRECTIONS FOR MOBILE TECHNOLOGY SUPPORTING ADAPTIVE AGING Several outcome criteria can be envisioned for assessing effectiveness of MMI systems as they mature, drawing on the RE-AIM framework (Glasgow, Vogt, and Boles, 1999) that was developed in the public health intervention field.
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though that period is lengthening, perhaps in response to smartphone cost increases and slowing improvement in functionality. Our suspicion is that aging adults may change phones less f requently, based on evidence that of those age 65 and older, 53 percent own smartphones and 39 percent own nonsmart cellphones compared to ages 18–29, where 96 percent own smartphones and 4 percent own nonsmart cellphones (Pew Research Center, 2019)
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From page 35... ...
Potential Research Priorities for MMI Technology Acceptability Even if an MMI system can show efficacy and cost effectiveness, its value for enhancing well-being in our aging population will be in jeopardy if it is not adopted and used. • Studies of adoption and use of MMI systems need extended time frames (e.g., decades)
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. Gerontechnology acceptance by elderly Hong Kong C hinese: A Senior Technology Acceptance Model (STAM)
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to promote physical activity. International Journal of Behavioral Nutrition and Physical Activity, 16(1)
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. Feasibility and acceptability of a wearable technology physical activity intervention with telephone counseling for mid-aged and older adults: A randomized controlled pilot trial.
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in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6)
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. The effect of digital physical activity interventions on daily step count: A randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study.
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