Predictors of the Use of Assistive Devices that Address Physical Impairments Among Community-Based Frail Elders University at Buffalo Machiko R. Tomita, Ph.D. William C. Mann, Ph.D. OTR/L Linda Fraas, MA, OTR/L Kathy Stanton, MS, RN

Introduction The majority of people who use ADs in the U.S. are adults age 65 and over (Russell, Hendershot, LeClere, & Howie, 1997) As the population of older adults increases, the importance of understanding the factors associated with the use of ADs also increases (Hartke, Prohaska, & Furner, 1998)

Between physical needs and device use to address physical impairments The types and number of devices used are directly related to the type and level of disability, and elders who are more physically disabled use more devices for physical impairments that compensate for their disabilities (Edwards & Jones, 1998; LaPlante, Hendershot, & Moss, 1992; Mann, Ottenbacher, Hurren, & Tomita, 1995) Therefore, among AD users, functional impairment serves as the single most significant health characteristic predictor for device use (Hartke et al., 1998)

Introduction Use of appropriate ADs combined with home environmental modifications leads to less decline in functional status among community-dwelling frail elders than elders who had regular home care services (Mann, Ottenbacher, Fraas, Tomita & Granger, 1999)

Purpose of the study To identify, beyond the need, important predictive variables to use ADs that address physical impairments among cognitively intact home-based frail elders.

Behavioral model applied to AD use Aday and Andersen (1974) and Andersen (1995) Characteristics of Population at Risk Need Factor Enabling Factor Predisposing Factor Use of Devices

Research Questions 1.Is physical disability level the strongest predictor of AD use than any other predictors? 2.Which physical health indicators (number of illnesses, number of medications taken, number of physician visits) are important predictors of use of devices to address physical impairments? 3.Which demographic variables among the predisposing factors (age, sex, race and education) and enabling factors (income, marital status, living arrangements, region, and social resources availability) are important predictors for use of ADs? 4.Which psychosocial variables (self-esteem and depression) are important predictors for use of ADs?

Methods Participants 694 elders without cognitive impairments The mean age: 76.5 years Female -76% White - 80%, Black - 20% Married - 29% Lived alone - 59% Less than 12 years of education – 59% Annual income of less than $10,000 – 45 % Western New York %; Northern Florida or southern Georgia %

Data gathering procedures for assistive device (AD) use It is an individualized measure that involves defining the purpose of device use in relation to a user’s disabilities. There are 15 categories among the types of ADs to address physical impairments. Interviewers visited participants’ homes, identified ADs in the homes and asked the participants if they used the device. A total of 6,587 devices in use were identified. The most often used devices were for bathing (19.7% of total devices used), devices for environmental control (12.1%), balance (11.5%), fine motor skills (10.7%) and meal preparation (10.5%). Methods

Types of Assistive Devices Used Methods DeviceFrequency (%) Number of Device/Person ADL- Bathing 1300 (19.7%)1.87 Environnemental Control Devices 799 (12.1%)1.15 Balance Aid 759 (11.5%)1.09 Fine Motor Devices 703 (10.7%)1.01 Meal Preparation 694 (10.5%)1.00 ADL-Hygiene 548 (8.3%)0.79 Special Phone Features & Accessories 466 (7.1%)0.67 Reachers 340 (5.2%)0.49 Wheelchair 220 (3.3%)0.32 Special Seating System 218 (3.3%)0.31 ADL- Dressing 16 (3.3%)0.31 Special Switches and Controls 100 (1.5%)0.14 Leisure 85 (1.3%)0.12 ADL- Eating 82 (1.2%)0.12 ADL- Grooming 57 (0.9%)0.08 Total6587 (100%)9.49

Statistical Analyses: Multiple regression with hierarchical method The 15 independent variables were: physical disability levels (SIP), 3 health indicators (number of medications taken, number of chronic illnesses/conditions, and number of physician visits in the last six months), 9 demographic variables (age, gender, region, race, education, annual income, living arrangements, social resources availability, and marital status), and 2 psychosocial variables (depression and self- esteem). Methods

Results Model A: Physical Disability as a predictor Intercept:5.305*** Physical Disability:.162 *** R-square:.139 Adjusted R2:.137 SEE: 5.923

Results Model B: Background factors as predictors Intercept: * Physical Disability:.184 *** (.423) Number of medications taken:.161 ** (.094) Race: *** (.172) Region: *** (.142) Living Arrangement: * (-.077) Education:.410 * (.071) R-square:.223 Adjusted R-square:.216 SEE:.650

Results Model C: Psychosocial factors as predictors Intercept: * Physical Disability:.207 *** (.476) Number of Medications Taken:.183 ** (.107) Race: *** (.172) Region: *** (.155) Living Arrangement: ** (-.088) Education:.344 (.060) Depression: ***(-.147) R-square:.241 Adjusted R-Square:.233 SEE:.590

Summary and Discussion 1.Among all of the variables, physical disability was the most significant predictor. It suggests that Aday and Andersen’s “need factors” be divided into primary need factor (physical disability level) and secondary need factor (the number of medications taken).

Summary and Discussion 2.Among demographic characteristics, race was the most important predictor, followed by region, number of medication and living arrangement. White elders who live alone in the south and who are taking more medications tend to use more devices to address physical impairments than non-White elders who are living with someone in the northern U.S. and are taking fewer medications.

Summary and Discussion 3.In this study, depression was not targeted to use of device, but it was found to be strongly associated with it. The likely pathway is that if elders are more depressed, they tend to use fewer devices. Using fewer devices, in turn, leads to decreased functional ability, which causes more depression.

Model of assistive device use Characteristics of Population at Risk Psychosocial Factor (Depression) Use of Assistive Device Predisposing Factor (Race) Enabling Factor (Region) (Living Arrangements) Secondary Need Factor (Number of Medications Taken) Primary Need Factor (Physical Disability Level) Summary and Discussion

Six predictors together accounted for 24% of the variance in the criterion variable. This still leaves 76% of the variance unexplained. Although motivation to use devices may account for another 4-5% of the variance of device use (Roelands et al., 2002), sources for 70% of the variance should be explored. Other sources may include severity and duration of chronic conditions, determination to live independently in a community, existence of social support, availability of health care aides, health care providers’ recommendation to use devices, knowledge of available ADs, interaction between device and home environment, and perceived encouragement / discouragement of device use.

Summary and Discussion Use of ADs is a type of health behavior among frail elders to maintain their independence and enable them to live at home. From the viewpoints of national health economy and quality of life of frail elders and caregivers, it is important to understand who does not use devices and why they do not. Further comprehensive studies to construct a model for maintaining health behavior including AD use (or non-use) is strongly encouraged.