Telerehabilitation is the delivery of rehabilitation services using information technologies covering a range of services, including assessment, monitoring, prevention, intervention, education, and counseling (American Telemedicine Association, 2010). Telerehabilitation holds great potential to support older adult independence at home and prevent rehospitalizations. However, telerehabilitation is still an emerging mode of practice.
The purpose of the current review is to (a) characterize current evidence about in-home telerehabilitation with older adults and (b) make recommendations for future telerehabilitation research to support older adults' independence at home.
PubMed, CINAHL, and Embase databases were searched using the following keyword combinations: [[“telerehabilitation” OR “tele-rehabilitation” OR [“informatics” and “rehabilitation”]] AND [“senior” OR “seniors” OR “older adult” OR “older adults” OR “elderly” OR “aging” OR “ageing”] AND [“home”]] for January 2005 to May 2015. Reference lists of systematic reviews were also reviewed (Kairy, Lehoux, Vincent, & Visintin, 2009; Rogante, Grigioni, Cordella, & Giacomozzi, 2010).
Inclusion criteria were English language articles that described telerehabilitation studies with older adult participants in their homes (average age ≥60 years), reported patient outcomes, and met quality of reporting criteria from the STARE-HI (Statement on Reporting of Evaluation Studies in Health Informatics) guidelines (Talmon et al., 2009).
Articles addressing clinic-based telerehabilitation, computational methods, system or technical descriptions, design methods, algorithms, architectures, conceptual models, study protocols, editorials, technology acceptance, cost analysis studies, theses, or dissertations were excluded. One hundred forty-seven titles and abstracts were independently reviewed by two authors (B.R., J.C.) using inclusion/exclusion criteria. Disagreements were reconciled through discussion. Twenty-three full-text articles were downloaded and independently reviewed following the same procedure. Sixteen articles were excluded during full-text review, leaving seven articles for inclusion. An eighth article (Wu & Keyes, 2006) was identified from reference list reviews. A ninth article (Moffet et al., 2015) was published just prior to completion of the review and was included. One author (B.R.) abstracted data from included articles.
Nine articles from four countries were included: Australia (n = 1) (Hoffmann & Russell, 2008), Canada (n = 2) (Moffet et al., 2015; Tousignant et al., 2011), the Netherlands (n = 1) (van den Brink et al., 2007), and the United States (n = 5) (Carey et al., 2007; Chumbler et al., 2012; Harada et al., 2010; Sanford et al., 2006; Wu & Keyes, 2006). Three studies were conducted through the United States Department of Veterans Affairs. Table 1 shows characteristics of included studies. Table 2 shows outcome measures and results of each study.
Design, Sample, Study Population, and Technology Type of Telerehabilitation Studies
Outcomes and Results of Telerehabilitation Studies
Six studies focused on delivery of telerehabilitation services by a remote therapist using real-time video, whereas three studies involved text- or web-based messaging and electronic surveys. Six of nine studies relied on dial-up Internet over plain old telephone service and three used broadband Internet connections for real-time video. For real-time video interventions, three of six studies had a research assistant present to monitor safety during telerehabilitation sessions; one used a family member or friend for this purpose (Tousignant et al., 2011) and two did not have a safety monitor present in the home (Moffet et al., 2015; Wu & Keyes, 2006). For all three broadband real-time video studies, in-home installation of Internet service was coordinated and provided to participants by the research team.
Dropout rates ranged from 8% to 21% for seven of eight studies reporting this statistic. For the eighth study, Harada et al. (2010) reported dropouts as percentages of participants who stopped communicating before the end of the intervention period; there was a dropout rate of 35.3% for the text intervention group, with decline in adherence rates after 8 weeks attributed to boredom due to daily surveys.
Most studies enrolled relatively healthy and independently living older adults. Exceptions to this were Chumbler et al. (2012), whose inclusion criteria for stroke survivors was need for minimal assistance in task performance, and Harada et al. (2010), whose participants were considered medically deconditioned based on self-reports of physical inactivity. A commonly reported limitation by authors of included studies was small sample size with recommendations for conduct of larger studies.
Only Chumbler et al. (2012) specifically reported no adverse events. Moffet et al. (2015) reported no serious adverse events attributed to the telerehabilitation intervention and two possibly related to the in-person intervention. Harada et al. (2010) reported the number of adverse events (e.g., chest pain/pressure, dizziness, trips, stumbles and falls), with a greater number occurring for the text intervention group than the telephone intervention group (mean = 2.1, SD = 2.9 versus mean = 0.1, SD = 0.5). The text intervention group also had higher levels of exercise adherence (Harada et al., 2010), suggesting differences in numbers of adverse events across groups may be attributed to amount of exercise performed.
Using author lists, composition of the research team in four of nine studies involved physical/occupational therapists, technologists, and physicians (Carey et al., 2007; Moffet et al., 2015; Sanford et al., 2006; Tousignant et al., 2011). Other studies were collaborations comprising: a physical therapist and technologist (Wu & Keyes, 2006), only physical and occupational therapists (Hoffmann & Russell, 2008), primarily physicians (Harada et al., 2010), and physicians and an informatician (van den Brink et al., 2007). Only one research team included an advanced practice nurse collaborating with physical therapists and physicians (Chumbler et al., 2012).
Real-time video telerehabilitation interventions are feasible, with evidence of improvements in physical function, self-efficacy, and quality of life. Patient self-reports using text devices, as well text messages received through these devices, are also feasible, with some evidence for improved exercise adherence and performance of instrumental and social tasks. Assessments by remote therapists compare favorably with those of therapists in the home.
In 2007, Russell (2007) outlined a typology with three types of telerehabilitation technologies: (a) image-based, (b) sensor-based, and (c) virtual reality. Text-based telerehabilitation interventions are not covered in the typology. Six of nine studies included in the current review were image-based, with one involving a sensor-based component (Hoffmann & Russell, 2008). No studies involved virtual reality technologies. Also absent in the included studies was the use of wearable and embedded sensor technologies for activity sensing, as well as mobile devices (e.g., tablets) to collect participant self-reports and support communication. Further, no included studies used an integrated technology approach using multiple technology types or sensing modalities coupled with tailored messaging. In addition, no studies of robotic-assisted rehabilitation at home were included as this is an emerging area of research.
Real-time telerehabilitation can improve access to rehabilitation services for patients in rural areas by eliminating travel time over geographic distances and facilitating continuity of care by maintaining the same therapists or care teams for in-patient and in-home visits (Sanford et al., 2006). However, recent technology innovations have the potential to improve past telerehabilitation interventions. For example, the Internet of Things, proposed as everyday objects with network connectivity that automate data capture (Ashton, 2009), is being realized with in-home research platforms, such as Lab of Things (Samuel, Bernheim Brush, & Mahajan, 2014) and Piloteur (Feminella, Pisharoty, & Whitehouse, 2014). These platforms have the potential to enable new modes of integrated in-home data collection and support for technology-based measures of health across post-acute care settings (Rantz et al., 2014; Reeder, Richard, & Crosby, 2015).
Use of consumer-grade wearable devices, mobile technologies, and embedded sensors connected through these types of platforms will allow immediate translation of research results in support of home-based health care delivery. This deluge of data sourced from connected devices will require novel visualization methods to summarize and present information to understand older adults' health status (Alexander, Wilbik, Keller, & Musterman, 2014; Reeder, Chung, Le, Thompson, & Demiris, 2014).
As technologies and rehabilitation approaches continue to evolve, it is critical to integrate them in practice to maximize technology support for emerging treatment strategies. In particular, many types of rehabilitation services require some level of in-home presence either (a) to allow for hands-on manual therapies or (b) for safety during activities that challenge mobility. Therefore, exclusively using real-time video for telerehabilitation interventions may not be feasible for all populations. However, combining a program with some on-site rehabilitation visits and some real-time telerehabilitation visits may maximize available resources. In cases where patients live in rural areas and may normally only be seen once per week by a member of the rehabilitation team, an on-site visit could be supplemented with one or two additional telerehabilitation interactions to extend the benefits of the intervention. In other cases, where hands-on visits are less critical, the total number of on-site visits could be reduced and replaced with more telerehabilitation visits to maximize efficient use of resources.
More regular telerehabilitation interactions may improve exercise adherence and overall physical activity and at the same time allow providers to proactively identify new health issues that require immediate attention. In an era of decreasing financial resources for patient care and increased constraints to health care reimbursement, maximizing patient adherence, creating opportunities for more effective therapies, and minimizing adverse health events is essential. Therefore, concerted efforts to integrate in-home telerehabilitation applications with evolving rehabilitation approaches that optimize care delivery are now more critical than ever.
There is a need for larger studies that translate telerehabilitation evidence into the real-world context of home health care workflows, processes, and information systems. These studies must recognize and anticipate challenges identified in telehomecare research, specifically those related to potential for increased workloads and numbers of in-person visits for telerehabilitation recipients (Bowles et al., 2011).
The current review excludes studies of emerging technologies with samples of <10 participants and published prior to 2005.
Future research should focus on team-based, multitechnology approaches that include wearable, mobile, and in-home sensing devices for patient health monitoring, self-reports, and goal setting, as well as fidelity to protocols in the delivery of telerehabilitation services. Given the interdisciplinary nature of post-acute care delivery, nurse scientists and practicing nurses, along with rehabilitation therapists, informaticians and other home health care team members, should be involved in the collaborative design and conduct of in-home telerehabilitation research for older adults. New research efforts should engage participants beyond those who are healthy and living independently to bring restorative benefits to older adults at greater risk for loss of independence or rehospitalization.
- Alexander, G.L., Wilbik, A., Keller, J.M. & Musterman, K. (2014). Generating sensor data summaries to communicate change in elders' health status. Applied Clinical Informatics, 5, 73–84. doi:10.4338/ACI-2013-07-RA-0050 [CrossRef]
- American Telemedicine Association. (2010). A blueprint for telerehabilitation guidelines. Retrieved from http://www.americantelemed.org/docs/default-source/standards/a-blueprint-for-telerehabilitation-guidelines.pdf?sfvrsn=4
- Ashton, K. (2009). That ‘internet of things’ thing. RFiD Journal, 22, 97–114.
- Bowles, K.H., Hanlon, A.L., Glick, H.A., Naylor, M.D., O'Connor, M., Riegel, B. & Weiner, M.G. (2011). Clinical effectiveness, access to, and satisfaction with care using a telehomecare substitution intervention: A randomized controlled trial. International Journal of Telemedicine and Applications, 2011, 12. doi:10.1155/2011/540138 [CrossRef]
- Carey, J.R., Durfee, W.K., Bhatt, E., Nagpal, A., Weinstein, S.A., Anderson, K.M. & Lewis, S.M. (2007). Comparison of finger tracking versus simple movement training via telerehabilitation to alter hand function and cortical reorganization after stroke. Neurorehabilitation and Neural Repair, 21, 216–232. doi:10.1177/1545968306292381 [CrossRef]
- Chumbler, N.R., Quigley, P., Li, X., Morey, M., Rose, D., Sanford, J. & Hoenig, H. (2012). Effects of telerehabilitation on physical function and disability for stroke patients: A randomized, controlled trial. Stroke, 43, 2168–2174. doi:10.1161/STROKEAHA.111.646943 [CrossRef]
- Feminella, J., Pisharoty, D. & Whitehouse, K. (2014). Piloteur: A lightweight platform for pilot studies of smart homes. Paper presented at the Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. , Memphis, TN. .
- Harada, N.D., Dhanani, S., Elrod, M., Hahn, T., Kleinman, L. & Fang, M. (2010). Feasibility study of home telerehabilitation for physically inactive veterans. Journal of Rehabilitation Research & Development, 47, 465–475. doi:10.1682/JRRD.2009.09.0149 [CrossRef]
- Hoffmann, T. & Russell, T.G. (2008). Pre-admission orthopaedic occupational therapy home visits conducted using the Internet. Journal of Telemedicine and Telecare, 14, 83–87. doi:10.1258/jtt.2007.070808 [CrossRef]
- Kairy, D., Lehoux, P., Vincent, C. & Visintin, M. (2009). A systematic review of clinical outcomes, clinical process, healthcare utilization and costs associated with telerehabilitation. Disability and Rehabilitation, 31, 427–447. doi:10.1080/09638280802062553 [CrossRef]
- Moffet, H., Tousignant, M., Nadeau, S., Mérette, C., Boissy, P., Corriveau, H. & Dimentberg, R. (2015). In-home telerehabilitation compared with face-to-face rehabilitation after total knee arthroplasty. Journal of Bone and Joint Surgery, 97, 1129–1141. doi:10.2106/JBJS.N.01066 [CrossRef]
- Rantz, M.J., Skubic, M., Popescu, M., Galambos, C., Koopman, R.J., Alexander, G.L. & Miller, S.J. (2014). A new paradigm of technology-enabled ‘vital signs’ for early detection of health change for older adults. Retrieved from http://eldertech.missouri.edu/files/Papers/Rantz/A%20New%20Paradigm%20of%20Technology-Enabled%20'Vital%20Signs'.pdf
- Reeder, B., Chung, J., Le, T., Thompson, H.J. & Demiris, G. (2014). Assessing older adults' perceptions of sensor data and designing visual displays for ambient environments: An exploratory study. Methods of Information in Medicine, 53, 152–159. doi:10.3414/ME13-02-0009 [CrossRef]
- Reeder, B., Richard, A. & Crosby, M.E. (2015). Technology-supported health measures and goal-tracking for older adults in everyday living. Foundations of Augmented Cognition, 9183, 796–806. doi:10.1007/978-3-319-20816-9_76 [CrossRef]
- Rogante, M., Grigioni, M., Cordella, D. & Giacomozzi, C. (2010). Ten years of telerehabilitation: A literature overview of technologies and clinical applications. Neuro-Rehabilitation, 27, 287–304. doi:10.3233/NRE-2010-0612 [CrossRef]
- Russell, T.G. (2007). Physical rehabilitation using telemedicine. Journal of Telemedicine and Telecare, 13, 217–220. doi:10.1258/135763307781458886 [CrossRef]
- Samuel, A., Bernheim Brush, A.J. & Mahajan, R. (2014). Lab of things: Building a research platform for connected devices in the home and beyond. GetMobile, 18, 37–40.
- Sanford, J.A., Griffiths, P.C., Richardson, P., Hargraves, K., Butterfield, T. & Hoenig, H. (2006). The effects of in-home rehabilitation on task self-efficacy in mobility-impaired adults: A randomized clinical trial. Journal of the American Geriatrics Society, 54, 1641–1648. doi:10.1111/j.1532-5415.2006.00913.x [CrossRef]
- Talmon, J., Ammenwerth, E., Brender, J., de Keizer, N., Nykänen, P. & Rigby, M. (2009). STARE-HI—Statement on reporting of evaluation studies in health informatics. International Journal of Medical Informatics, 78, 1–9. doi:10.1016/j.ijmedinf.2008.09.002 [CrossRef]
- Tousignant, M., Moffet, H., Boissy, P., Corriveau, H., Cabana, F. & Marquis, F. (2011). A randomized controlled trial of home telerehabilitation for post-knee arthroplasty. Journal of Telemedicine and Telecare, 17, 195–198. doi:10.1258/jtt.2010.100602 [CrossRef]
- van den Brink, J.L., Moorman, P.W., de Boer, M.F., Hop, W.C., Pruyn, J.F., Verwoerd, C.D. & van Bemmel, J.H. (2007). Impact on quality of life of a telemedicine system supporting head and neck cancer patients: A controlled trial during the postoperative period at home. Journal of the American Medical Informatics Association, 14, 198–205. doi:10.1197/jamia.M2199 [CrossRef]
- Wu, G. & Keyes, L.M. (2006). Group tele-exercise for improving balance in elders. Telemedicine and e-Health, 12, 561–570. doi:10.1089/tmj.2006.12.561 [CrossRef]
Design, Sample, Study Population, and Technology Type of Telerehabilitation Studies
|Authors||Study Design||Sample Completed/Enrolled||Study Population||Technology Type|
|Sanford et al. (2006)||Randomized controlled trial||n = 16/23 (telerehabilitation); n = 16/18 (in-person); n = 33/41 (usual care controla)||Mobility-impaired community-dwelling older adults||Real-time video over plain old telephone service (POTS)|
|Wu & Keyes (2006)||Feasibility study||n = 14/17||Balance-impaired older adults||Real-time video over 256 kbps broadband Internet connection|
|Carey et al. (2007)||Randomized crossover trial||n = 10/12 (track); n = 10/12 (move)||Post-stroke survivors with hand paralysis||Real-time video over POTS and patient hand sensor control of cursor|
|van den Brink et al. (2007)||Prospective controlled trial||n = 35/39 (telerehabilitation); n = 128/145 (usual care controlb)||Head and neck cancer surgery patients||Web-based messaging and health information over POTS|
|Hoffman & Russell (2008)||Feasibility study||n = 40/40||Pre-admission orthopedic surgery patients||Real-time video over POTS and sensor-based height measurements|
|Harada et al. (2010)c||Feasibility study||n = 10/16d (text intervention); n = 5/15d (phone intervention)||Physically inactive older adults||Text messaging device over POTS|
|Tousignant et al. (2011)||Randomized controlled trial||n = 21/24 (telerehabilitation); n = 20/24 (usual care controle)||Total knee arthroplasty patients||Real-time video over 512 kbps broadband Internet connection|
|Chumbler et al. (2012)||Randomized controlled trial||n = 22/24 (telerehabilitation); n = 22/24 (usual care controlf)||Post-stroke survivors||Text messaging device over POTS, recorded video|
|Moffet et al. (2015)||Randomized controlled trial||n = 94/104 (telerehabilitation); n = 98/101 (usual care controlg)||Total knee arthroplasty patients||Real-time video over 512 kbps broadband Internet connection|
Outcomes and Results of Telerehabilitation Studies
|Sanford et al. (2006)||Self-efficacy by Falls Efficacy Scale||Changes in self-efficacy scores for combined intervention groups were more than seven times greater than those for the no-therapy control group. Medium effect sizes for in-person and telerehabilitation interventions. Telerehabilitation just missed significance.|
|Wu & Keyes (2006)||TUG; MOS SF-36; ABC; single leg stance time; body sway during quiet stance||Participants showed significant improvements in balance and reduction of fear of falling. All participants had positive views of the program and most were able to operate video equipment independently within the first week.|
|Carey et al. (2007)||Box and Block grasp and release; Jebsen Taylor hand function; finger range of motion; finger movement tracking; whole brain fMRI during finger movement tracking||Both groups improved in function but the track group did not surpass the move group and gains were not linked to brain reorganization. Functional gains in the track group may not have reached full potential due to compressed training duration and shortened rest times as compared to prior studies.|
|van den Brink et al. (2007)||19 validated cancer QoL subscales; three non-validated, study-specific QoL subscales||At 6 weeks, five of 22 QoL subscales showed significant improvement for the intervention group. At 3 months, only physical self-efficacy remained significantly different.|
|Hoffman & Russell (2008)||Assessment of four patient transfer tasks; 27-item Home Environment Questionnaire developed at study hospital; furniture height measurements||One hundred percent agreement between remote and in-person therapists for four transfer tasks and 98.9% agreement for the 27-item home assessment survey. Small differences in furniture height measurements. Ten patients operated equipment independently.|
|Harada et al. (2010)||Safety by number of self-reported adverse events||Participants using text had significantly better intervention adherence and exercise adherence rates with greater satisfaction than telephone users. Text users had a dropoff in adherence rates after 8 weeks.|
|Tousignant et al. (2011)||Knee range of motion using goniometry; Berg balance scale; 30-second chair stand; WOMAC; TUG; POMA; SMAF||Both groups showed significant improvements in disability and knee function 1 week after assessments, with no significant intergroup differences. Improvement in knee function and functional activities at 2 months with control showing greater improvement.|
|Chumbler et al. (2012)||FONEFIM motor subscale; LLFDI Overall Function Component||Changes in self-reported physical function and disability for both groups were not significantly different. However, the intervention group significantly improved more than the control group in ability to perform and manage instrumental and social tasks.|
|Moffet et al. (2015)||WOMAC (with 9% inferiority margin)||Differences between groups for pain, stiffness, function, and QoL were near zero 4 months after discharge. Intervention adherence was high, with 85% of telerehabilitation and 99% of the control group receiving at least 75% of 16 targeted sessions.|