Technology and Aging

Hearing loss, the partial or total inability to perceive sound (Bance, 2007), is the third leading chronic illness in older adults, exceeded only by hypertension and arthritis (Hannula, 2011). In nursing homes, the prevalence of hearing loss is staggering, with reports between 82%-90% (Cohen-Mansfield & Taylor, 2004). More than 77% of nursing home residents with hearing loss have not purchased hearing aids or any other amplification device (Cohen-Mansfield & Taylor, 2004; Pryce & Gooberman-Hill, 2012). Hearing loss greatly interferes with communication, impeding the ability to impart health information (Cohen-Mansfield & Taylor, 2004; Pryce & Gooberman-Hill, 2012). Untreated hearing loss detracts from interactions with family, cognitive status, functional status, and social integration (Gopinath et al., 2012; Lin et al., 2011; Schneider et al., 2010; Shah et al., 2011; Solheim, Kvaerner, & Falkenberg, 2011).

Amplification has many benefits. Hearing aids improve audiometry scores by 15-20 decibels (dB), a measure of sound intensity. Aids improve hearing handicap scores by an average of 55% (Kochkin, 2011), and improve speech understanding, especially in one-on-one situations (Lewis, 2006). People who hear better and communicate more effectively experience less depression, anxiety, fear, isolation, and cognitive decline (Kiebling & Kreikemeier, 2013). Those who adopt hearing aids participate more in leisure activities than those with uncorrected hearing loss (Gonsalves & Pichora-Fuller, 2008). Those who wear hearing aids show improved cognition scores over those with uncorrected hearing loss
(Cruz-Oliver, 2014).

Many methods exist to assist in communication and speech understanding. These include amplifiers (such as frequency modulator (FM) systems or pocket talkers), dry erase boards, electronic boards, sign language, and writing notes. (Lancioni et al., 2012; Shinohara, 2012; Shinohara & Wobbrock, 2011). Older adults become hard of hearing with age and so have not learned sign language. Writing notes or using dry erase boards and electronic boards can be cumbersome and time consuming.

This study aimed to explore the acceptability and use of FM systems among long-term care residents and staff.

Lane, K., Rantz, M., Rawn, C., & Bien, A. (2015). Are older persons willing to accept and use amplifiers to better understand speech? Clinical Gerontologist, 38(5), 351-358.

When planning the Aging in Place Initiative at TigerPlace, it was envisioned that advances in technology research had the potential to enable early intervention in health changes that could assist in proactive management of health for older adults and potentially reduce costs.

The purpose of this study was to compare length of stay (LOS) of residents living with environmentally embedded sensor systems since the development and implementation of automated health alerts at TigerPlace to LOS of those who are not living with sensor systems. Estimate potential savings of living with sensor systems.

Rantz, M.J., Lane, K.R., Phillips, L.J., Despins, L.A., Galambos, C., Alexander, G.L., Koopman, R.J., Skubic, M., & Miller, S.J. (2015). Enhanced RN care coordination with sensor technology: impact on length of stay and cost in Aging in Place housing. Nursing Outlook, 63, 650-655.

We present an example of unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes. Sensors embedded in the environment capture behavior and activity patterns. Changes in patterns are detected as potential signs of changing health. We first present results of a preliminary study investigating 22 features extracted from in-home sensor data. A 1-D alert algorithm was then implemented to generate health alerts to clinicians in a senior housing facility. Clinicians analyze each alert and provide a rating on the clinical relevance. These ratings are then used as ground truth for training and testing classiers. Here, we present the methodology for four classication approaches that fuse multisensor data. Results are shown using embedded sensor data and health alert ratings collected on 21 seniors over nine months. The best results show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. Finally, we propose a health change detection model based on these results and clinical expertise. The system of in-home sensors and algorithms for automated health alerts provides a method for detecting health problems very early so that early treatment is possible. This method of passive in-home sensing alleviates compliance issues.

Skubic, M., Guevara, R., & Rantz, M. (2015). Automated health alerts using in-home sensor data for embedded health assessment. IEEE Journal of Translational Engineering in Health and Medicine, 3, 1-11.

Objective: Our purpose was to describe how we prepared 16 nursing homes (NHs) for health information exchange (HIE) implementation.
Background: NH HIE connecting internal and external stakeholders are in their infancy. U.S. initiatives are demonstrating HIE use to increase access and securely exchange personal health information to improve patient outcomes.
Method: To achieve our objectives we conducted readiness assessments, performed 32 hours of clinical observation and developed 6 use cases, and conducted semi-structured interviews with 230 participants during 68 site visits to validate use cases and explore HIE.
Results: All 16 NHs had technology available to support resident care. Resident care technologies were integrated much more with internal than external stakeholders. A wide range of technologies were accessible only during administrative office hours. Six non-emergent use cases most commonly communicated by NH staff were: 1) scheduling appointments, 2) laboratory specimen drawing, 3) pharmacy orders and reconciliation, 4) social work discharge planning, 5) admissions and pre-admissions, and 6) pharmacy-medication reconciliation. Emerging themes from semi-structured interviews about use cases included: availability of information technology in clinical settings, accessibility of HIE at the point of care, and policies/procedures for sending/receiving secure personal health information.
Conclusion: We learned that every facility needed additional technological and human resources to build an HIE network. Also, use cases help clinical staff apply theoretical problems of HIE implementation and helps them think through the implications of using HIE to communicate about clinical

Alexander, G.L., Rantz, M., Galambos, C. Vogelsmeier, A., Flesner, M., Popejoy, L.L., Mueller, J., Shumate, S., & Elvin, M. (2015). Preparing nursing homes for the future of health information exchange. Applied Clinical Informatics, 6, 248-266.*

Falls are a major problem for the elderly leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The fall risk and detection system includes pulse-Doppler radar, a Microsoft Kinect, and two web cameras. Stunt actors performed falls in apartments each month for 2 years and participants completed a series of monthly fall risk assessments (FRAs) using standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants spatial and temporal gait parameters were measured as they walked on a GAITRite mat. Results of validation analyses are presented that compared radar and Kinect generated gait variables to ground truth data (FRAs and GAITRite variables). All FRAs are highly correlated with the Kinect data including gait velocity, stride length and stride time. Radar velocity is correlated to all FRAs and highly correlated to most. Algorithms were developed for automated detection of falls and measuring fall risk. Real-time alerts of actual falls are being generated and sent to clinicians in the facility providing faster responses to urgent situations. The in-home fall risk assessment and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.

Rantz, M., Skubic, M., Abbott, C., Galambos, C., Popescu, M., Keller, J., Stone, E., Back, J., Miller, S.J., & Petroski, G.F. (2015). Automated in-home fall risk assessment and detection sensor system for elders. The Gerontologist, 55(S1), S78-S87.

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