The world’s population is getting older. This demographic trend has long-term effects on the economic welfare of nations, as older patients consume the greatest proportion of health care dollars (Orszag, 2008). Currently, approximately 36% of health care expenditures have been allocated to provide personal medical care to older adults, and future estimates keep escalating (Keehan, Lazenby, Zezza, & Catlin, 2004; Potez, Cubanski, & Neuman, 2011; Truffer et al., 2010). Research to date has proposed several ways to contain the accelerated costs of care for older adults based on early diagnosis, monitoring, and intervention in vulnerable groups. Evidence shows that early interventions aimed at restoring impaired physiological homeostasis can prevent adverse health-related consequences (e.g., falls, morbidity, mortality, disability, hospitalization), even among the oldest-old group (Faber, Bosscher, Chin, Paw, & van Wieringen, 2006; Fiatarone et al., 1994; Pahor et al., 2006; Province et al., 1995). Thus, it is imperative to identify the most vulnerable subset of the older population who could significantly benefit from targeted interventions. Based on these assumptions, researchers have been extensively searching for the tangible measurements of physiological vulnerability in the aging population. The concept of frailty has been developed to identify this group of older adults at greatest risk of adverse events.
Frailty is conceptually defined as a state of excessive vulnerability to environmental stressors with substantial decrease in physiological reserves across multiple systems (Fried et al., 2001). The concept of frailty has been discussed in the geriatric literature for more than 3 decades, gradually making the transformation from a mere definition of the phenomenon to a distinct medical syndrome with its own etiology, symptoms, and pathophysiological mechanisms. Its origin can be traced to the late 1960s, when O’Brien, Roberts, Brackenridge, and Lloyd (1968), in their cross-sectional study of community-dwelling older adults, outlined frailty as a gradual process characterized by excessive disproportional reaction of older adults to adverse events. After this first publication, the concept of frailty rarely appeared in medical literature until 1988, when Winograd, Gerety, Brown, and Kolodny suggested its first quantitative measurement, operationalizing frailty as the presence of one or more of 15 common geriatric clinical conditions. Based on a cross-sectional study of 1,200 institutionalized older adults, this study found that older hospitalized patients were not uniformly frail and that a screening process for frailty should be multidimensional (Winograd et al., 1988).
Considerable progress in understanding and exploring frailty occurred when Fried et al. (2001) empirically defined the frailty phenotype as the accumulation of three of five physiological deficits (muscle weakness, low gait speed, weight loss, exhaustion, and low physical activity). This definition was formulated from community-dwelling older adults participating in the Cardiovascular Health Study (CHS; N = 5,317) (Fried et al., 2001). The authors suggested that the presence of two of the indicators defined a “pre-frail” state, and the presence of three or more indicators corresponds to frailty. The published results showed that this “phenotype” was strongly associated with falls, worsened mobility, disability, hospitalization, and death. Applying similar criteria, other epidemiological studies have revealed that 16.3% of community-dwelling older women are frail and 28.3% considered pre-frail (Woods et al., 2005); the prevalence of frailty in the Women’s Health and Aging population exceeded 11% (Bandeen-Roche et al., 2006). The Frailty Task Force of the American Geriatrics Society adopted the CHS’ working definition of frailty as the currently used operational definition (Lang, Michel, & Zekry, 2009).
Although the CHS frailty phenotype has been validated and modified for use in several published reports (Santos-Eggimann, Cuénoud, Spagnoli, & Junod, 2009; Sarkisian, Gruenewald, Boscardin, & Seeman, 2008; Woods et al., 2005), limitations remain that challenge its generalizability and usefulness in the clinical setting (Lang et al., 2009). In addition, numerous researchers argue that frailty is a multidimensional and multisystem process that could not been comprehensively captured by applying physical criteria only, as there are other social, cognitive, neurological, and biological domains that should be taken into consideration (Lang et al., 2009). This lack of consensus on the definition of the construct of frailty and its components has been discussed in a number of articles (Abellan van Kan et al., 2008; Avila-Funes et al., 2009; Lang et al., 2009; Rothman, Leo-Summers, & Gill, 2008), leading researchers to keep investing effort to forge a comprehensive model of frailty. Furthermore, most of the coined operational definitions of frailty require continuous assessment by clinically trained staff in either health care facilities or community settings; thus, frailty assessment on an ongoing basis would be costly, time consuming, and in many cases, not feasible. Emerging information technologies (ITs), on the other hand, could provide an effective, flexible, and integrative solution for passive monitoring and measuring the different aspects of frailty construct in real-life settings, helping to enhance and expand frailty assessment.
In tandem with the rapid growth of an aging population, the world is witnessing the wide expansion of assistive informational and robotic technologies, which have been pervasively incorporated into real-life settings. Emerging technologies range from simple devices, which are able to carry out a single function, to sophisticated telecommunication technologies and robotic machines, thus offering tremendous opportunity to redefine the role of community-based care in today’s health environments. For instance, telehealth technologies have been used to provide a wide range of diagnostic and home-monitoring services for management of chronic conditions in older adults. In-home health monitoring has been associated with improved outcomes for patients with several chronic conditions, including heart failure (Dansky & Vasey, 2009; Polisena et al., 2010; Shah, Der, Ruggerio, Heidenreich, & Massie, 1998), cancer (Chumbler et al., 2007), diabetes (Shea et al., 2009; Stone et al., 2010) as well as those with multiple chronic conditions (Darkins et al., 2008). Substantial progress in the field of robotics has also led to the integration of robotic assistive devices in the care of individuals with disabilities, promoting enhanced mobility, improved independence in activities of daily living (ADLs), and increased ability to communicate (Brose et al., 2010). Given the widespread nature of these technological advances in other related fields, those interested in frailty, including researchers, clinicians, patients, and caregivers, might also benefit from the integration of innovative technological tools in emerging research.
The purpose of this article is to discuss how ITs have been or may potentially be used to measure common elements of frailty, identified from a systematic review of the literature. Strengths and limitations of various methods and areas for future work are also discussed.
An integrative review was conducted to systematically address the following research questions: (a) What are the common physiological indicators of frailty that have been used in multiple research studies? and (b) What are the main clinical domains that have been integrated into operational definitions of frailty?
Based on the synthesis of the results of the first two questions, the authors further investigated the actual and potential integration of ITs into emerging frailty research, emphasizing strengths and limitations of various methods, and outlined areas for future work.
A systematic search of the literature was conducted using PubMed and CINAHL from June 1999 through June 2009. In PubMed, the search strategy used the medical subject headings (MeSH) frail elderly and any one of the terms geriatric assessment, diagnosis, diagnostic, questionnaire, or measure. To avoid duplication with PubMed results, the literature search in CINAHL excluded PubMed/MEDLINE records and was limited to peer-reviewed research articles published in English. Titles and abstracts of articles extracted by the search were reviewed for their relevance to the review topic. If potentially relevant, the full-text article was retrieved. Studies were included in the review if they fulfilled the following criteria: (a) the study described a research design in which data had been collected in at least one time point, (b) the authors explicitly addressed the way in which frailty status had been defined and measured, (c) the statistical analysis aimed at addressing the magnitude of association between frailty and health-related outcomes, (d) sample size had adequate power to statistically address the differences between and within frailty groups, (e) research participants were community-dwelling older adults, and (f) published in English.
The MEDLINE search yielded 1,921 hits, whereas the CINAHL database returned 26 results. A total of 1,815 records were discarded on the basis of title and abstract. One hundred thirty-two articles were relevant to the study purposes, the full texts of which were retrieved for further examination. Fifty-six papers met all inclusion criteria; however, 48 articles were excluded from the final analysis due to redundancy (Figure). Given that the conducted literature review aimed at mapping frailty indicators that have been used in epidemiological studies, each of its distinct sets has been included only once in the final analysis. In other words, the final table includes only studies that proposed a unique set of frailty markers. For instance, Fried et al.’s (2001) frailty phenotype that proposed the constellation of five physiological indicators to measure frailty was included only once in the final analysis, regardless of the fact that it was the most commonly used approach in multiple cohorts. The final analysis included eight research papers (Table 1) from a number of different countries, including Canada (n = 2), The Netherlands (n = 1), and the United States (n = 5). Once these studies were identified, they were analyzed to identify common elements of frailty, which were then examined for the ability to measure the element using ITs.
Figure. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart of study selection process to identify articles that address physiological indicators of frailty.
Table 1: Studies Included in Final Review
To identify actual and potential applications of ITs for frailty research, we pursued multiple sources to ensure a comprehensive search for possible technologies. Noteworthy, this article does not provide an exhaustive review of the technologies themselves. The actual and potential ITs were located using an Internet search, expert panel, and bibliographic references. We focused on a comprehensive analysis of existing technologies that have been described in indexed or online reports. The type of technology, its characteristics, and present-day use in health care research were extracted and documented. The name and the type of technological solution were then entered in PubMed/MEDLINE, and results were reviewed to find evidence of published reports on its validity and reliability. If no published reports on the reliability of measurement were located, the reference provided is the URL link to one of the corporate websites marketing the technology.
The main markers comprising a clinical operational definition of frailty can be condensed into distinct categories according to the main physiological parameter measured: musculoskeletal function, physical functioning, neurocognitive function, respiratory function, energy metabolism, and coexisting medical conditions (Table 2).
Table 2: Markers of Frailty and Their Operational Definitions
Impairment in the motor system was the most commonly measured parameter across multiple studies; at least one type of musculoskeletal measurement was integrated into the working definition of frailty in each of the reviewed studies. Functional assessment of the motor system has been based on multiple tests measuring either upper/lower extremity strength or each of these parameters individually (e.g., hand-grip strength, timed gait speed, chair stand). Physical functioning, as measured by impairment in at least one of the ADL parameters, was addressed in three of eight operational definitions of frailty. Neurocognitive factors were identified as important in half of the reviewed papers. In frailty research, neurocognitive parameters include a wide array of factors such as cognitive impairments, sensory impairments (e.g., visual, hearing), and psychological factors (e.g., depression). Respiratory functioning as measured by peak expiratory flow rate using the Mini Wright Peak Flow Meter or maximal oxygen consumption (VO2 max) was included in three of the identified studies. Energy metabolism measurements, including nutritional status and energy level assessment, were identified in five of eight of the reviewed studies. Nutritional status was measured using weight and body mass index changes over a certain period of time (mostly 1 to 2 years). Reduced energy levels and total energy expenditure were assessed based on self-report scales. Finally, coexisting conditions such as chronic health disorders, incontinence, and sleep disorders were included in three working definitions of frailty and represented the total number of self-reported chronic health conditions and geriatric syndromes.
Table 3 provides evidence that emerging ITs have the potential to be an effective and flexible solution to comprehensively capture the various aspects of frailty identified from the systematic review. IT also has the ability to capture this information across multiple settings, including private residences and long-term care facilities. As technology has advanced, many of the applications have now become widely available to both older adults as well as formal and informal caregivers. Although much of the identified technology is currently commercially available, research regarding issues of reliability and validity implementation, interoperability, ethical considerations, and policy implications is limited.
Table 3: Current and Potential Technologies to Measure Indicators of Frailty
Discussion and Implications
Frailty is an emerging multidimensional gerontological clinical syndrome that refers to an excessive vulnerability of an aging organism to environmental challenges (Fried, Waltson, & Ferrucci, 2009). This process develops gradually in a step-wise pattern, with increments of decline triggered by acute events (Ahmed, Mandel, & Fain, 2007). A number of assessment protocols have been tested to measure frailty. Although each of these protocols is rooted in a distinct conceptual model of frailty and incorporates a unique constellation of physiological parameters, we discerned six common clinical domains across studies: musculoskeletal function, physical functioning, neurocognitive function, respiratory function, energy metabolism, and coexisting medical conditions.
Musculoskeletal attributes indirectly measure the manifestations of sarcopenia, which has been proposed as the central etiological mechanism of frailty (Evans et al., 2010; Fried et al., 2009; Rolland et al., 2008). Impairments in physical functioning have been operationalized as self-reported difficulties in performance of ADLs (Villareal, Banks, Siener, Sinacore, & Klein, 2004; Jones, Song, Mitnitski, & Rockwood, 2005; Rockwood, Andrew, & Mitnitski, 2007), and respiratory functioning has been evaluated using tests of peak expiratory flow rate (Klein, Klein, Knudtson, & Lee, 2005; Puts, Lips, & Deeg, 2005) and peak oxygen uptake levels (Villareal et al., 2004). There are differences of opinion whether impairment in physical functioning is a cause or a consequence of frailty. This disagreement was addressed in a recent review by Abellan van Kan et al. (2008), which suggested that a disability should be treated as an outcome. Neurocognitive parameters incorporate assessment of cognitive, sensorial, and mood impairments based on well-validated self-report instruments (Jones et al., 2005; Puts et al., 2005; Rockwood et al., 2007). Energy metabolism is also suggested as one of the key pathophysiological mechanisms of frailty, as it has been hypothesized that chronic undernutrition, enhanced by age-related changes in body composition, results in a syndrome of excessive physical vulnerability (Ensrud et al., 2008; Fried et al., 2009; Rockwood et al., 2007).
Reliability and Validity. From Table 3, it is clear that additional research needs to be conducted to evaluate the reliability and validity of IT-based measurement of frailty parameters, such as lower muscle functioning, unobtrusive energy expenditure, and measurements of ADL performance. For instance, a variety of technologies are available in prototype forms for evaluating physical functioning (e.g., ADLs) in real-life settings. However, not all of these technologies have been shown to be valid and reliable for use in frail older adults. This may be due to the early stage of development of pervasive behavioral monitoring devices, where the main focus has been on feasibility of measurements rather than on content validity in a certain population.
Another point to consider in the context of reliability and validity of technology-based measurements is the challenge of false-positive and false-negative results. Relying on devices that detect abnormalities that are not really present or do not accurately detect patterns would have serious financial and psychological consequences. Furthermore, these might negatively affect the acceptability of the technology to both patients and their caregivers. The greater the discrepancy between actually observed parameters and what has been measured using technology solutions, the higher the probability that potential users and stakeholders (e.g., nurses, clinicians, families) will avoid using or responding to technology (i.e., alert fatigue) in the future.
As predicted, the number of well-validated technologies for traditional physiological monitoring (e.g., muscle strength, peak expiratory power) was larger than the number of validated technologies tailored to unobtrusively monitor functional status. Less information was available on transferring self-report items to digital formats. Currently available paper-and-pencil self-report questionnaires could be easily customized into computerized formats with features such as computer-adapted testing. Although this area has promise for reducing participant burden, additional research is needed to evaluate the reliability of these forms in alternative digital formats.
Implementation. Implementation refers to the extent to which an intended use of a technology requires a certain level of extra training or infrastructural changes (Bernard & Tichkiewitch, 2010). The reviewed technological solutions ranged from small ambient sensors to stand-alone analytical units (e.g., gait analysis system, respiratory digital measurement systems). Comparing implementation aspects of the various technologies, stand-alone units need to be operated by end users and therefore require a certain level of training. Although stand-alone units have been extensively used in community settings and clinical environments, they have provided information primary related to physiological measurements. Alternatively, sensor-based systems can be integrated into existing infrastructure and do not, in principle, require training of or operation by the patient, but may require training of the researcher or clinician conducting the monitoring (Demiris & Hensel, 2008). Cognitive and psychological assessments are relatively new technological solutions that have yet to be further explored.
Ethical Considerations. Integration of assistive technologies into a community setting inextricably depends on user acceptance. A number of factors could have a direct impact on one’s decision to use technologies. These include considerations such as confidentiality, privacy, and obtrusiveness of the measurement (Demiris, 2009). Confidentiality relates to the concern that a third party or other stakeholders could potentially access personal health information without getting proper consent from a user. Privacy concerns pertain to the extent to which the health-related data can be collected without revealing personal information to an unauthorized party. Given that most digital technologies rely heavily on wireless data transmission and long-term data storage, it is imperative that the privacy and confidentiality of electronic health information be ensured as the information is maintained, processed, and transmitted electronically. Ethical concerns related to privacy and confidentiality apply as pervasively to younger populations as they do to older adults; however, in older adults, the perceived need for an assistive technology may mediate the effect of privacy concerns when making a decision about technology adoption (Courtney, Demiris, Rantz, & Skubic, 2008; Demiris, 2009; Demiris et al., 2006).
A broader aspect to be taken into consideration that has a direct impact on adoption and sustained use of technology in real life is obtrusiveness. Conceptually defined, obtrusiveness is “a summary evaluation by the user based on characteristics or effects associated with the technology that are perceived as undesirable and physically and/or psychologically prominent” (Hensel, Demiris, & Courtney, 2006, p. 430). For instance, ubiquitous sensor-based monitoring occasionally requires infrastructural changes and extensive wiring that might be perceived as excessively obtrusive and noticeable. This may lead potential users to abandon its use. Thus, integrating technology into the assessment of frailty will be conditional based on the interplay between the perceived needs and ethical considerations of potential users and how they are addressed. Future research is needed to provide additional insight into this issue.
Interoperability. Frailty research will require multi-system assessment and will rely heavily on the process of data analysis and data integration. This requirement places interoperability issues as a priority concern that should be explicitly addressed when designing the architecture of systems aimed at comprehensively assessing frailty. Interoperability refers to the ability of diverse systems to work in synchrony with each other. With respect to health IT, the term interoperability is used to describe the capability of different technological solutions to exchange data using the same protocol (López & Blobel, 2009). Consequently, a lack of interoperability results in difficulty integrating data from multiple sources into a single, large database. In this integrative review, we found little published information that discusses interoperability of systems; therefore, it is an aspect that requires further investigation.
Policy Implications. Additional considerations that need to be addressed relate to the various roles of stakeholders, regulators, and other interested parties in the process of technology dissemination. Hammel (2004) suggested that cost and lack of sufficient financial support to pay for devices are one of the key factors that affect the use of assistive technologies in the community setting. It has been estimated that 2.5 million Americans who reported a need for assistive technology were unable to obtain it, mostly due to lack of affordability (Hammel, 2004). These findings clearly indicate that public health regulation and reimbursement policies have a direct effect on the integration of technology in the community setting. If there is no central mechanism to assist in payments for use of certain health-related technology, there is a low likelihood that this technology will find its way out to the broad market.
Another important aspect that has a direct impact on the integration of technologies in the community setting from the perspective of stakeholders is cost effectiveness. In the context of assistive technologies, the cost effectiveness is the ratio of the costs (financial and time) of data collection to its benefits (e.g., prevention of disability, morbidity). Thus, cost refers to the resources allocated for the data collection, and the measure of effect is expressed as the fraction of prevented conditions. It has been found that prompt detection of frailty processes and recognition of frailty itself have the potential of postponing and preventing multiple severe consequences, such as repeated falls, fractures, hospitalization, institutionalizations, and death (Fried et al., 2009; Gill et al., 2002; Lang et al., 2009). Consequently, frailty screening in real-life settings may be cost effective or even cost saving in nature. The population most likely to benefit initially from technology assessment and monitoring are those who are pre-frail or who have recently transitioned to frail state, but this is also an area for future study. Furthermore, data on cost effectiveness will enable stakeholders to make informed decisions with regard to reimbursement policies.
Unfortunately, scarce information is currently available on the cost effectiveness of technologies for aging. The upsurge in newly available technological solutions for monitoring older adults requires further economic evaluation. The last policy implication pertains to legal accountability for the collected data. Policy makers and legislators need to explicitly determine who “owns” health information, who has access to this information, and finally, what the legal and moral responsibilities would be for that individual or entity (e.g., patient, family, health care provider, health care facility), given the extensive flow of data.
Aging in Place. Older adults want to age at home and remain independent for as long as possible. Emerging ITs hold great promise to extend the services that once were reserved for health care facilities to older adults’ homes, facilitating the aging-in-place paradigm (Tyrer et al., 2006). In general, the concept of aging in place is to allow older adults to remain in the environment of their choice with supportive services as needed (Marek & Rantz, 2000). The technologies discussed in Table 3, such as environmental and motion sensors, sensory assessment software, and computerized forms, have potential to promote independence and enhance the transition to the aging-in-place model even among physically vulnerable older adults. For instance, IT can be used to monitor and identify early changes in physiological indicators (e.g., mobility, vital signs) that are the precursors of declining health status (Demiris, Thompson, Reeder, Wilamowksa, & Zaslavsky, 2011).
Health care providers in general—and nurses in particular—can use information about changes in health status to manage and eventually improve the quality of care for older adults. However, the widespread use of technologies inextricably depends on the technical, ethical, and health policy considerations that need to be explicitly addressed in future research. Nurses caring for older adults are in the most advantageous position to plan and conduct feasibility studies examining reliability and validity of the existent technologies, to uncover the concerns that may impede the acceptance of technologies, and finally, to advocate for broader use of technologies across settings, addressing health care policy decision makers, regulators, and other stakeholders.
An operational definition of frailty composed of several distinct clinical domains was identified from a systematic review of the literature. The domains identified have been shown to predict an increased risk of future morbidity, disability, and death in frail older adults. IT can potentially be integrated into frailty research to assist patients, clinicians, and other caregivers in community settings to promptly detect and monitor the processes of physical vulnerability. Several types of technologies are readily available for use, whereas others will require some modification and further evaluation prior to widespread adaption. Additionally, further research is needed to (a) address the ethical, technical, clinical, and economic issues that have been challenging the prompt dissemination of technology into both aging research and clinical practice and (b) identify the population of older adults (pre-frail versus frail) who are most likely to benefit from this technology. Nurses should take an active part in bringing these technologies into practice through ongoing collaboration, meticulous evaluation, and evidence-based research.
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Studies Included in Final Review
|Reference/Sample||Upper Extremity Strength||Lower Extremity Strength||Peak Expiratory/Aerobic Power||Energy Expenditure||Energy Level||Nutritional Status||Cognitive Function||Depression Symptoms||Sensory Function||Coexisting Medical Conditions||Performance of ADLs|
|Fried et al. (2001) (N = 5,317)||X||X||X||X||X|
|Gill et al. (2002) (N = 188)||X|
|Villareal, Banks, Siener, Sinacore, and Klein (2004) (N = 156)||X||X||X|
|Jones, Song, Mitnitski, and Rockwood (2005) (N = 2,305)||X||X||X||X||X||X||X|
|Klein, Klein, Knudtson, and Lee (2005) (N = 2,962)||X||X||X||X|
|Puts, Lips, and Deeg (2005) (N = 2,473)||X||X||X||X||X||X||X|
|Rockwood, Andrew, and Mitnitski (2007) (N = 2,305)||X||X||X||X||X||X||X|
|Ensrud et al. (2008) (N = 6,701)||X||X||X|
Markers of Frailty and Their Operational Definitions
|Category/Marker||Objective Test Performed||Operational Definition|
|Upper extremity strength||Hand-grip strength||Grip strength in the lowest quintile stratified by BMI and sex (Fried et al., 2001) or sex only (Klein, Klein, Knudtson, & Lee, 2005)|
|Lower extremity strength||Chair stand||Inability to rise from a chair three to five times without using arms (Ensrud et al., 2008; Gill et al., 2002; Jones, Song, Mitnitski, & Rockwood, 2005; Klein et al., 2005)|
|Timed gait speed||Timed walking speed in the lowest quintile (Gill et al., 2002) stratified by sex (Klein et al., 2005) and height (Fried et al., 2001)Reported impaired mobility (Rockwood, Andrew, & Mitnitski, 2007)|
|Performance of ADLs||Disability in one or more ADL parameter (Jones et al., 2005; Rockwood et al., 2007; Villareal, Banks, Siener, Sinacore, & Klein, 2004)|
|Cognitive functioning||Cognitive impairment as measured by the MMSE (Puts, Lips, & Deeg, 2005) or evaluated as part of CGA (Jones et al., 2005)|
|Depressive symptoms||The accumulation of depressive symptoms, as measured by standardized tools (e.g., CES-D Scale [Puts et al., 2005]; GDS [Jones et al., 2005])|
|Sensory functioning||Distal visual acuity||Best corrected visual acuity of 20/40 or poorer in the better eye (Klein et al., 2005); deficit in vision (Jones et al., 2005)Deficit in hearing capacity (Jones et al., 2005; Puts et al., 2005)|
|Peak expiratory/aerobic power||Peak expiratory flow rate||Peak expiratory flow rate as measured by Mini Wright Peak Flow Meter (Klein et al., 2005; Puts et al., 2005)|
|Peak oxygen uptake levels||Maximal oxygen consumption (VO2 max) (Villareal et al., 2004)|
|Energy expenditure||Self-reported energy expenditure in the lowest quintile (Puts et al., 2005) stratified by sex (Fried et al., 2001)|
|Nutritional status||Weight measurement||Unintentional weight loss of ⩾5% of body weight (Fried et al., 2001; Jones et al., 2005), or ⩾10 pounds in 1 year (Fried et al., 2001); BMI <23 at baseline (Puts et al., 2005); weight loss of ⩾5% of body weight in 2 years (Ensrud et al., 2008)|
|Reduced energy level||Self-reported exhaustion (Ensrud et al., 2008; Fried et al., 2001)|
|Coexisting medical conditions|
|Comorbidities||Accumulation of reported comorbidities (Jones et al., 2005; Rockwood et al., 2007) and geriatric syndromes (Puts et al., 2005)|
Current and Potential Technologies to Measure Indicators of Frailty
|Category/Marker||Technology Used to Measure/Assess||Current or Potential Use/Limitations|
|Upper extremity strength||Computerized portable dynamometer (Bellace, Healy, Besser, Byron, & Hohman, 2000; Shechtman, Gestewitz, & Kimble, 2005) capable of transmitting data to a server||CurrentLimitation: limited capability of data transmission|
|Lower extremity strength||Gait analysis system (Barker, Craik, Freedman, Herrmann, & Hillstrom, 2006; Bilney, Morris, & Webster, 2003; Cutlip, Mancinelli, Huber, & DiPasquale, 2000; Gorelick, Bizzini, Maffiuletti, Munzinger, & Munzinger, 2009; Hayes, Hagler, Austin, Kaye, & Pavel, 2009; Wang et al., 2009)||CurrentCapable of measuring spatiotemporal gait parameters (e.g., walking speed, cadence, step length, single- and double-limb-support duration and stride width for individual steps and the step sequence)Of note: Walking speed has been used as a proxy measurement for lower extremity function (Guralnik, Ferrucci, Simonsick, Salive, & Wallace, 1995)Limitation: expensive|
|Environmental sensors (Hayes, Hagler, Austin, Kaye, & Pavel, 2009)||Sensor-based system capable of continuous assessment of walking speed in home environmentLimitation: Limited information on reliability|
|Muscle strength analysis system (Bolliger, Banz, Dietz, & Lünenburger, 2008; DIERS Biomedical Solutions, n.d.)||PotentialExpensive prototype technologyLimitation: Limited information on reliability|
|Floor pressure sensors measuring lower extremity strength||Potential|
|Performance of ADLs||Sensor-based monitoring (Dalton, Morgan, & Olaighin, 2008; Le, Di Mascolo, Gouin, & Noury, 2008; Logan, Healey, Philipose, Munguia-Tapia, & Intille, 2007; Zhou et al., 2009)||PotentialPrototypes of smart home-based health platform for behavioral monitoringLimitation: limited information on reliability and validity|
|Cognitive functioning||Computerized standardized forms; cognitive assessment software solutions (Dwolatzky et al., 2003; Haimov, Hanuka, & Horowitz, 2008; Heinik, Werner, Dekel, Gurevitz, & Rosenblum, 2010; Merrick, Secker, Fright, & Melding, 2004)||Current|
|Depressive symptoms||Computerized standardized forms (Fliege et al., 2009; Johnson, Vincent, Johnson, Gilliland, & Schlegel, 2008)||Current|
|Sensory functioning||Sensory assessment software solutions (Kumar, Bulsara, & Yogesan, 2008; Smits, Kapteyn, & Houtgast, 2004)||Current|
|Peak expiratory power||Respiratory function digital measurement systems (FDI Medical, n.d.; Fonseca et al., 2005; Medgraphics, n.d.; Wiltshire & Kendrick, 1994)||Current|
|Energy expenditure||Wearable devices: motion sensors (e.g., accelerometers [Welk, Schaben, & Morrow, 2004]), pedometers, integrative systems (Berntsen et al., 2008; Brage, Brage, Franks, Ekelund, & Wareham, 2005; Rodriguez, Brown, & Troped, 2005)||CurrentAccelerometer capable of measuring daily physical activity (e.g., intensity, frequency, duration, total volume)Pedometer capable of measuring walking-related activitiesIntegrative system combines motion sensors with technologies such as heart rate recorder, global positioning system unit, postural changes sensors, and skin response sensorsLimitation: Nonwear time should be taken into consideration|
|Nonwearable devices: environmental sensors providing proxy measurement of total energy expenditure (Skubic, Alexander, Popescu, Rantz, & Keller, 2009)||Motion sensors distinguish sedentary activity from mobility episodesLimitation: limited information on reliability|
|Nutritional status||Weight scales (Goldstein & Levin, 2001; Tamura et al., 2007) capable of transmitting data to a server||Current|
|Reduced energy level||Computerized standardized forms (Johnson et al., 2008; Whitehead, 2009)||Current|
|Coexisting medical conditions|
|Electronic medical record; personal medical record||Current/potential|