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Impact of telemonitoring on older adults health-related qualityof life: the Tele-ERA study

Jennifer L. Pecina Gregory J. Hanson

Holly Van Houten Paul Y. Takahashi

Accepted: 21 January 2013 / Published online: 14 February 2013 Springer Science+Business Media Dordrecht 2013

AbstractPurpose Telemonitoring is being increasingly used forchronic disease monitoring. While the primary aim of te-lemonitoring is to improve chronic disease managementand decrease hospitalizations, the potential impact onpatients health-related quality of life may be an additionalbenet. Methods Two hundred and ve patients aged 60 yearsand older with multiple medical conditions were enrolledin a one-year randomized controlled trial of daily hometelemonitoring. Health-related quality of life was measuredwith the 12-Item Short-Form at the beginning and at thecompletion of the study. Per protocol analysis of the 166patients responding to the follow-up survey was performed. Results Among the 166 responders, there were no sig-nicant differences at baseline in the physical componentsummary (PCS) scores ( p value = 0.32), nor the mentalcomponent summary (MCS) scores ( p value = 0.12)between the telemonitored group and the usual care group.

There was also no difference in the 12-month PCS scores( p value = 0.39) or MCS scores ( p value = 0.10) betweengroups. There was no difference in the change from base-line to 12-month MCS scores between groups ( p value =0.89); however, there was a signicant difference in thebaseline to 12-month change of PCS scores betweengroups, with the telemonitored group having a greaterdecrease in PCS scores ( - 4.3 9.3), compared to theusual care group ( - 1.2 8.5) over the course of the study( p value = 0.03).Conclusion Home telemonitoring in older adults withmultiple comorbidities does not signicantly improve self-perception of mental well-being (as measured by MCSscores) and may worsen self-perception of physical health(as measured by PCS scores).

Keywords Health-related quality of life Hometelemonitoring Telemedicine Geriatrics

Introduction

Chronic disease management utilizes a large percentage of health care expenditures. In the United States (US) in 2001,chronic disease care accounted for 83 % of health carespending [ 1]. The risk for chronic disease increases withage, and 2 out of every 3 persons aged 65 years and oldersuffer from multiple chronic illnesses [ 1]. By 2030, it isestimated that 20 % of the US population will consist of people 65 years and older with chronic medical conditions[1].

Our current medical system is not equipped to providechronic disease care to all that need it, and new models fordelivery of care are needed [ 2]. Telemedicine and telem-onitoring are methods that have been proposed to assist in

J. L. Pecina ( & )Department of Family Medicine, Mayo Clinic, 200 First StreetSW, Rochester, MN, USAe-mail: [email protected]

G. J. Hanson P. Y. TakahashiDivision of Primary Care Internal Medicine, Mayo Clinic,Rochester, MN, USA

H. Van HoutenHealth Care Policy and Research, Mayo Clinic, Rochester, MN,USA

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providing this care [ 3]. Home telemonitoring is the use of telecommunication technologies to monitor a patientsclinical status from their home. Studies of home telemon-itoring in asthma, diabetes, heart failure, and hypertensionhave found it useful in the management of these conditions[4, 5].

Health-related quality of life (HRQOL) is used tomeasure effects of chronic illness on patients daily lives.Measurements of HRQOL in clinical trials are increasinglybeing used to assess the effects of interventions on apatients illness. The Agency for Healthcare Research andQuality (AHRQ) Task Force on Aging has highlighted theneed for health services research in the care of older peo-ple, due to its emphasis on studies related to maximizingfunction and health-related quality of life [ 6].

To date, the effect of home telemonitoring on health-related quality of life has been mixed. In studies of te-lemonitoring effects on HRQOL in single chronic diseasemanagement, improved quality of life was reported in atelemonitoring study of insulin-treated diabetics [ 7]; how-ever, a randomized controlled trial of telemonitoring inpatients with chronic obstructive pulmonary disease did notshow any difference in quality of life scores between themonitored group and the control group [ 8]. These studiesevaluated individual groups rather than a population of complex medical patients.

There are few studies evaluating the effect of home te-lemonitoring on quality of life in patients with multiplecomorbidities; however, studies of these populations showsimilarly mixed results. In a small, randomized controlledtrial of 37 patients (average age 69) comparing home healthcare nursing visits, versus home health care nursing visitscombined with telehealth, there was a statistically signi-cant improvement in the mental health component of HRQOL after 6 months, compared to the usual care group[9]. In contrast, a randomized study of 104 elderly patients(average age 71) with multiple comorbidities comparinguse of a home telehealth unit, versus usual care, did not ndany difference between groups in functional level orHRQOL, despite a statistically signicant reduction in beddays of care and Emergency Department (ED) care [ 10].One might expect that improved disease managementthrough telemonitoring would improve HRQOL as well ashealth outcomes such as hospitalizations and ED visits.There is a need to have a larger study to evaluate theeffectiveness of telemonitoring upon HRQOL in olderadults with complex medical problems.

We assessed the effect of a home telemonitoring inter-vention on patients health-related quality of life. The Tele-ERA trial is a randomized controlled trial of home telem-onitoring in patients aged 60 years and older with multiplemedical comorbidities [ 11 , 12].

Methods

Design

This was a per protocol secondary analysis of a randomizedcontrolled trial performed at an academic medical center inRochester, MN [ 12]. The study was reviewed and approved

by the Institutional Review Board at Mayo Clinic.

Participants

Inclusion criteria included patients aged 60 years and olderwith an Elderly Risk Assessment (ERA) score in thehighest decile. The ERA score is a composite score of previous hospitalizations, age, race, and presence of chronic disease. A high ERA score indicates an increasedrisk for hospitalization and ED visits [ 13]. Exclusion cri-teria included patients living in a nursing home or with ahistory of dementia. Patients were also excluded if theywere unable or unwilling to use the monitoring equipment,or if there was a concern for undiagnosed dementia aftermental status testing.

Intervention

The specic intervention has been described in the pub-lished protocol [ 11 ]. Patients were randomized into usualcare or daily home telemonitoring and followed for oneyear. Monitoring was done with the Intel Health Guide ,which collects biometric data (blood pressure, weight,pulse, temperature, pulse oximetry, peak ows) andadministers symptom questionnaires with the goal of earlydetection of decline in health status. The device also allowsvideoconference visits. Results of the daily monitoringwere reviewed by a nurse. Any concerning results wereaddressed either by the nurse, a videoconference visit witha geriatric nurse practitioner, a message to or visit with thepatients primary care provider, or referral to the ED asappropriate.

Outcomes

The primary outcome was the physical and mental score onthe Short Form Health Questionnaire (SF-12v1 ) at thecompletion of the study at 12 months. The SF-12v1 is avalidated 12-question survey that measures a patients self-reported health-related quality of life. It is designed to take,on average, less than 2 minutes to complete. The surveyconsists of a Physical Component Summary (PCS) and aMental Component Summary (MCS) surveying social andphysical functioning, bodily pain, vitality, and generalmental health [ 14]. The SF-12 scoring was designed to

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have a mean score of 50 in the general US population [ 14].Higher scores indicate a higher level of health, with 100being the highest possible score obtainable. Scores for thePatient Health Questionnaire 9 (PHQ 9), Barthel index andself-reported health status were also collected. All param-eters were collected at baseline, 6 and 12 months. The12-month HRQOL results are the focus of this study asthese evaluate HRQOL at the completion of the studyintervention.

Analysis

Univariate analyses were performed to obtain descriptivestatistics of the individual variables. Measures of associa-tion were tested using bivariate analyses (two sample t testsfor continuous and Chi-square test for categorical vari-ables). All analyses for this study were done using the SASstatistical software (SAS version 9.2 for Windows; SASInstitute Inc. Cary, North Carolina).

Results

Two hundred and ve patients were randomized into thestudy. All 205 patients completed the SF-12 at baseline(102 telemonitoring group, 103 usual care group). At12 months, 166 patients completed a SF-12 form(responders) and 39 patients did not (non-responders). Of the 39 patients who did not complete the 12-month SF-12,19 died during the trial, 19 opted out of the trial, and 1patient returned the survey unanswered. For the 19 whoopted out, reasons given included consent withdrawn (13),living circumstances changed (4), and death that occurredafter the study, but before the SF-12 was collected (2). Wecompared non-responders versus responders, and there wasno difference in baseline age, gender, ERA scores, meangrip strength, or mean timed up and go test betweenresponders and non-responders. Compared to non-responders, responders had a mean gait speed that wassignicantly faster (0.73 vs 0.54 m/s with p value \ 0.01), alower mean reported self-health status (3.2 vs 3.8 with p value \ 0.01) and a higher mean Barthel score (95.2 vs91.8 with p value of 0.04) (Table 1). We do not havebaseline data on mean grip strength (1 patient), timed upand go test, and the mean gait speed (12 patients) due topatients inability to perform the tests. The focus of thisstudy is the 166 patients that responded to the 12-monthsurvey.

In the responder group, there was no difference inbaseline age, gender, ERA scores, PHQ 9 scores, BarthelScores, Kokmen short test of mental status scores, meangrip strength, timed up and go test, or mean gait speedbetween the intervention and usual care group (Table 2).

Among the 166 responders, there were no signicant dif-ferences at baseline in PCS scores ( p value = 0.32) norMCS scores ( p value = 0.12) between groups. At6 months, there were no differences noted in the total PCSscores or the change in PCS scores. There was a minimallysignicant difference in 6 month MCS scores with theusual care group having a higher MCS score (59.1 vs 56.8with a p value of 0.04), but there was no difference in thechange in MCS scores between groups at 6 months(Table 3). There was no difference in 12-month PCS scores( p value = 0.39) or MCS scores ( p value = 0.10) betweengroups. Comparing the change from baseline to 12-monthMCS scores between groups, there was no difference( p value = 0.89). However, there was a signicant differ-ence in the baseline to 12-month change of PCS scoresbetween groups, with the telemonitored group having agreater decrease in PCS scores ( - 4.3 9.3) compared tothe usual care group ( - 1.2 8.5) over the course of thestudy ( p value = 0.03) (Table 3).

There was no difference in 6- or 12-month PHQ 9scores, self-reported health status, Kokmen short test of mental status scores or Barthel scores between the inter-vention and usual care group (Tables 4, 5, 6, 7).

Discussion

In this secondary analysis of a randomized controlled trialof telemonitoring of at-risk older adults, we found thatthere was no difference in 12-month PCS and MCS scoresbetween groups. Both groups had improvement in theirMCS scores and worsening of their PCS scores over thecourse of the study, with the telemonitored group having asignicantly greater decline in PCS scores with an averagedecrease of 4.3 points, versus an average decrease of 1.2points in the usual care group ( p value 0.03).

This small decline is of borderline statistical signicancegiven multiple comparisons; however, it is likely clinicallymeaningful to note the decline in physical quality of life. Inthe original analysis of this cohort looking at hospitaliza-tion and emergency room visits, we found a lack of ef-cacy of telemonitoring to reduce hospitalizations oremergency room visits. In fact, mortality was higher in thetelemonitored group [ 12]. Thus, telemonitoring did notimprove quality of life or reduce health care outcomes andmay have caused challenges with increased mortality and adecrease in PCS scores.

The nding of a greater decline (although of borderlinesignicance) in PCS scores in the telemonitored group wasnot expected, and we do not have a denite explanation forthis nding. The baseline measurements of functional sta-tus were similar between the telemonitored group and theusual care group. There was no difference in self-rated

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health status, ability to perform activities of daily living orPHQ 9 scores at baseline, 6 or 12 months. Thus, it does notappear that functional status, depression, or self-ratedhealth status account for the difference. One might hopethat telemonitoring would improve self-perception of physical quality of life through continuous monitoring,allowing earlier intervention to prevent decline in healthstatus; however, increased monitoring also has the potentialto lead to increased medical interventions that have the

potential for negative iatrogenic effects. It is unclearwhether increased medical interventions could account fora worsening in patients self-perception of their physicalhealth compared to the usual care group.

Whether increased self-attention to ones symptoms(through daily monitoring sessions) could lead to worse self-perceptions of physical health is another possibility to con-sider. The monitoring in our study included daily questionson patients symptoms, including whether symptoms were

Table 1 Comparison of responders versus non-responders

All Non-responders Responders p value

Number of patients 205 39 166

Mean age (SD) 79.8 (8.2) 81.4 (8.9) 79.4 (8.0) 0.16

Mean ERA score (SD) 17.7 (5.8) 18.6 (4.2) 17.5 (6.1) 0.20

Gender, n(%) 0.50

Female 111.0 (54.2) 23.0 (59.0) 88.0 (53.0)Male 94.0 (45.8) 16.0 (41.0) 78.0 (47.0)

Mean grip strength (SD)a

18.5 (9.0) 16.6 (6.4) 18.9 (9.4) 0.06

Mean timed up and go (SD)b

14.6 (12.0) 15.9 (6.2) 14.3 (12.9) 0.49

Mean gait speed, m/s (SD) c 0.70 (0.37) 0.54 (0.18) 0.73 (0.39) \ 0.01

Mean PHQ9 (SD) 3.7 (3.8) 3.5 (3.4) 3.8 (3.9) 0.65

Mean Self-Rated Health (SD) 3.3 (0.90) 3.8 (0.87) 3.2 (0.86) \ 0.01

Mean Kokmen score (SD)d

34.4 (2.3) 34.4 (2.3) 34.5 (2.3) 0.94

Mean Barthel score (SD) e 94.6 (8.9) 91.8 (9.5) 95.2 (8.7) 0.04a 1 missing grip strength (non-responder)b

12 missing timed up and go (5 non-responders; 7 responders)c

12 missing gait speed (5 non-responders; 7 responders)d 2 missing Kokmen score (1 non-responder; 1 responder)e

1 missing Barthel score (non-responder)

Table 2 Demographics of responders

Responders Intervention Usual care p value

Number of patients 166 77 89

Mean age (SD) 79.4 (8.0) 79.6 (8.7) 79.2 (7.4) 0.73

Mean ERA score (SD) 17.5 (6.1) 17.7 (6.3) 17.3 (5.8) 0.73

Gender, n(%) 0.38

Female 88.0 (53.0) 38.0 (49.4) 50.0 (56.2)

Male 78.0 (47.0) 39.0 (50.7) 39.0 (43.8)

Mean grip strength (SD) 18.9 (9.4) 18.5 (9.2) 19.3 (9.6) 0.58

Mean timed up and go (SD)a

14.3 (12.9) 12.6 (7.3) 15.7 (16.0) 0.14

Mean gait speed, m/s (SD)b

0.73 (0.39) 0.75 (0.42) 0.71 (0.36) 0.50

Mean PHQ9 (SD) 3.8 (3.9) 4.0 (3.9) 3.6 (3.9) 0.49

Mean Self-Rated Health (SD) 3.2 (0.86) 3.1 (0.90) 3.2 (0.83) 0.85

Mean Kokmen score (SD)c

34.5 (2.3) 34.4 (2.2) 34.5 (2.4) 0.73

Mean Barthel score (SD) 95.2 (8.7) 95.0 (10.0) 95.3 (7.5) 0.80a 7 missing timed up and go (6 intervention; 1 usual care)b

7 missing gait speed (6 intervention; 1 usual care)c 1 missing Kokmen score (usual care)

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worsening. In patients treated for hypertension, highersymptom counts have been associated with lower HRQOLscores, suggesting that patients with more symptoms ratetheir self-perceived HRQOL scores lower [ 15]. Whetherincreased awareness of or attention to symptoms (via daily

monitoring) could also impact HRQOL negatively isunknown, but is in the realm of possibility.

Previous studies looking at quality of life in patientsusing telemonitoring and usual care have also shown mixedeffectiveness. In a study of 44 obstructive pulmonary dis-ease patients, the authors did not nd a difference betweenthe telemonitoring group and the usual care group [ 16]. Inthe Cochrane metaanalysis of congestive heart failurepatients, 3 of 6 telemonitoring studies that included end-points of quality of life showed improvement in quality of

life both in physical and emotional components [ 17].Quality of life measurements evaluating telemonitoring ina mixed complex population have been less well denedcompared to individual disease states. In a study of medi-cally complex patients using telemonitoring versus usualcare, there was no difference in quality of life (measured asself-rated health) or patient satisfaction between telemon-itoring and usual care [ 10]. The use of telemonitoring inpatients with multiple illnesses and symptoms couldpotentially improve quality of life by reducing the burdenof illness, detecting (and addressing) declines in functional

status earlier and improving self-efcacy. Our study datadid not support this possibility as we saw no difference orimprovement in self-rated health, functional status (mea-sured by Barthels index), or HRQOL measures. The majorstrength of our study is that, to our knowledge, it is thelargest to date to study a home telemonitoring interventionin a complex, frail population of older adults. Although ourstudy numbers were powered for the primary outcomes, wecalculate that to obtain 80 % power to detect a 20 %increase in the baseline PCS score of 37.1, we would need

Table 3 Comparison of SF-12 scores at baseline, 6- and 12-month


Number of patients

166 77 89

Baseline

Physical 36.2 (10.6) 37.1 (9.9) 35.5 (11.1) 0.32

Mental 56.0 (7.9) 55.0 (8.5) 56.9 (7.2) 0.126-month

Physical 35.5 (11.3) 35.2 (11.0) 35.8 (11.6) 0.72

Mental 58.0 (6.9) 56.8 (7.2) 59.1 (6.5) 0.04

Difference: baseline to 6-month

Physical - 0.83 (9.1) - 2.0 (9.0) 0.2 (9.2) 0.14

Mental 1.5 (7.4) 0.95 (7.3) 2.0 (7.6) 0.40

12-month

Physical 33.6 (10.4) 32.8 (10.6) 34.2 (10.2) 0.39

Mental 57.1 (8.2) 56.0 (8.9) 58.1 (7.6) 0.10

Difference: baseline to 12-month

Physical - 2.6 (9.0) - 4.3 (9.3) - 1.2 (8.5) 0.03

Mental 1.1 (9.1) 1.0 (9.6) 1.2 (8.7) 0.89

Table 4 Comparison of PHQ9 scores at baseline, 6- and 12-month


Number of patients

166 77 89

Baseline 3.8 (3.9) 4.0 (3.9) 3.6 (3.9) 0.49

6-month 1.1 (2.6) 1.0 (3.2) 1.2 (1.9) 0.76

Difference:baseline to6-month

- 2.6 (3.8) - 3.0 (4.5) - 2.2 (3.1) 0.19

12-month 3.1 (4.1) 3.1 (4.1) 3.1 (4.2) 0.92


- 0.69 (5.0) - 0.88 (5.2) - 0.53 (4.9) 0.65

Table 5 Comparison of self-rated health at baseline, 6- and12-month


Number of patients

166 77 89

Baseline 3.2 (0.86) 3.1 (0.90) 3.2 (0.83) 0.85

6-month 3.1 (0.95) 3.1 (1.0) 3.1 (0.88) 0.92Difference:

baseline to6-month

- 0.03 (0.83) 0.0 (0.86) - 0.06 (0.80) 0.66

12-month 3.2 (0.94) 3.2 (1.0) 3.1 (0.90) 0 .55


0.03 (0.82) 0.09 (0.88) - 0.02 (0.77) 0.38

Based on 5-point Likert scale where 1 = excellent and 5 = poor

Table 6 Comparison of Kokmen score at baseline, 6- and 12-month


Number of patients

165 77 88

Baseline 34.5 (2.3) 34.4 (2.2) 34.5 (2.4) 0.73

6-month 34.4 (3.1) 34.4 (2.7) 34.5 (3.4) 0 .84


- 0.13 (2.8) - 0.15 (2.6) - 0.12 (3.0) 0.94

12-month 33.3 (5.0) 33.7 (3.1) 32.9 (6.2) 0 .34


- 1.2 (4.3) - 0.75 (2.4) - 1.6 (5.4) 0.19

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17 patients in the intervention group. We calculate that wecurrently have over 95 % power with 77 patients in theintervention arm and a * 12 % decrease in PCS score frombaseline to 12 month.

Limitations have been discussed in more detail else-where, but include the inability to blind patients to theintervention and potential issues with generalizability dueto the study population being composed of primarily per-sons of Northern European descent, as well as access to atertiary health care setting for both groups of patients [ 12].

Though difcult to form a rm conclusion on the effectof telemonitoring on HRQOL due to the heterogeneity of interventions and populations previously described in theliterature, our study adds information to the evidence basesuggesting that telemonitoring does not improve HRQOLin older patients with multiple comorbidities and, in fact,

may worsen self-perceptions of the physical component of health-related quality of life. As there is some evidence insupport of telemonitoring improving HRQOL [ 7, 9], it maybe that certain types of telemonitoring or certain types of populations may benet more than others. In our study,there was no rehabilitation or life style coaching compo-nent for the telemonitoring intervention. The addition of these services could potentially provide some benet onHRQOL for future clinical and research applications of telemonitoring.

Conclusion

Home telemonitoring in older adults with multiplecomorbidities does not signicantly improve self-percep-tion of mental well-being (as measured by MCS scores)and may worsen self-perception of physical health (asmeasured by PCS scores).

Acknowledgments This study was funded by Mayo Clinic institu-tional funds for clinical support, by grant 1 UL1 RRo24150 from theNational Center for Research Resources of the National Institutes of

Health (NIH) and by the NIH Roadmap for Medical Research. TheIntel Health Guide telemonitors and support were provided by CareInnovations (Intel-GE).

References

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Table 7 Comparison of Barthel score at baseline, 6- and 12-month


Number of patients

166 77 89

Baseline 95.2 (8.7) 95.0 (10.0) 95.3 (7.5) 0.80

6-month 95.2 (9.6) 94.7 (11.9) 95.6 (7.1) 0.55


- 0.25 (7.5) - 0.47 (7.6) - 0.06 (7.3) 0.73

12-month 91.9 (14.9) 90.5 (16.5) 93.1 (13.4) 0.28


- 3.3 (11.7) - 4.5 (11.8) - 2.2 (11.5) 0.21

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