Assessing physical activity in inpatient rehabilitation—sensor-based validation of the PAIR
© European Group for Research into Elderly and Physical Activity (EGREPA) 2014
Received: 31 January 2013
Accepted: 11 January 2014
Published: 23 January 2014
Increased physical activity is positively associated with better health in community-dwelling older persons. It is unclear whether physical activity also influences success of inpatient rehabilitation. For the assessment of physical activity in inpatient rehabilitation the Physical Activity in Inpatient Rehabilitation Assessment (PAIR), a short questionnaire based on five questions, was developed and preliminary validated. In this study, the PAIR was validated against a sensor-based physical activity measurement. Seventy functionally impaired and cognitively mostly intact patients of a German geriatric inpatient rehabilitation clinic who had undergone hip surgery (n = 62 women, median age = 83 years) participated. Physical activity was measured using the PAIR and a sensor-based activity monitor (Physilog ®; BioAGM, CH). Assessments were conducted at admission (T1) and 2 weeks later (T2) during the rehabilitation process. To assess concurrent and predictive validity, Spearman correlations and linear regression models were calculated using sensor-based walking activity and uptime activity (walking and standing time) as dependent variables. Criterion-related concurrent validity using physical activity sensors was weak to moderate. Correlations were slightly higher at T2 (r = 0.45-0.53) than at T1 (r = 0.44-0.46). The objectively measured variance of physical activity, explained by the PAIR, ranged from 25 to 43 %. PAIR activity scores and sensor-based walking or total activity increased in a dose-dependent manner, confirming the scoring system of the PAIR. The application time was usually less than 2 min. The validity of the PAIR is weak to moderate when compared to a sensor-based activity monitor and comparable to existing physical activity assessments for community-dwelling older adults.
Physical activity (PA) has been shown to be beneficial for diverse aspects of physical and mental health in community-dwelling older adults [10, 29]. It has been included in functional assessments concerning the aspect of physical frailty  and there is growing interest to use PA as one parameter to describe progress in rehabilitation [18, 23]. Former studies have used proxies to calculate frailty scores  or functional parameters with floor or ceiling effects to describe progress in rehabilitation.
Measurement of PA in inpatients has, however, been always difficult. Prescribed exercises do not reflect activity in its participation sense as mentioned in the International Classification of Functioning Disability and Health (ICF) . Patterson and colleagues  observed six patients for 12 h in inpatient rehabilitation and found very low activity levels between treatment sessions. An overall PA score was not constructed. In a recent publication in inpatients, PA (as measured by actigraphy) predicted physical function levels in hip fracture patients .
The question whether questionnaire-based instruments or performance-based/sensor-based approaches better reflect “real” PA levels has always been and still is a matter of debate. With regard to the direct PA measurement, the doubly labelled water method is usually seen as the gold standard in adults . However, this method is not feasible for everyday use. Therefore, newer approaches include sensor-based technology to be worn on the wrist [14, 19], on the trunk [2, 28] or on the legs . Yet, these tools can be expensive and acceptability is limited due to cumbersome attachment techniques as mentioned above. Therefore, sensors might not be readily available in any rehabilitation unit. Although many different questionnaires for community-dwelling older adults have been proposed and validated , an interview-based and validated assessment to measure PA in inpatients did not exist. The only tool mentioned in recent literature, a questionnaire on self-initiated and treatment-associated mobility that was asked every day during the hospital stay, has been associated with different health outcomes in two earlier studies [6, 41]. A previous validation of this instrument was not reported.
The Physical Activity in Inpatient Rehabilitation assessment (PAIR) was developed to allow an estimation of PA levels in inpatients without the use of technical equipment . The idea of the PAIR was to provide a cost-saving and widely available tool which is easy to implement within the rehabilitation process. An initial validation study against physical functioning as a proxy measure for PA has demonstrated reasonable validity and good sensitivity to change despite its very short administration time .
Because most current tools for community-dwelling older adults have been validated using different gold-standard activity measurement techniques, we aimed to improve validation quality by choosing mobility sensors that include triaxial accelerometers and a gyroscope to assess both walking activity and uptime activity. With regard to criteria established by Murphy in 2009 , sensors were chosen because of the expertise of the research team, clinical availability and because they can validly and reliably capture exercise and lower mobility activity in older adults .
The aim of this study was to examine the concurrent validity of the PAIR against a sensor-based PA measurement in a sample of inpatient older adults able to walk at baseline and to compare practicality of the PAIR and the sensor with regard to application time.
Participants and design
A consecutive sample of 70 geriatric patients from the MEMBeR study  was recruited in a geriatric rehabilitation centre in the southwest of Germany between September 2007 and August 2010. The inclusion criteria were being 65 years or older and admittance for rehabilitation after hip fracture or elective hip replacement. Exclusion criteria were presence of major depression, severe cognitive or mental disorder, severely impaired vision and insufficient understanding of the German language as checked by the responsible senior consultant at admission. Patients were screened on admission and asked whether they would agree to participate. All participants gave written informed consent. The study was approved by the ethical committee of the local university.
Assessments were performed 1 day (physical performance, questionnaires) and 2 days (PA assessment) after admission (T1) and 2 weeks later (T2) in the rehabilitation centre. All assessments were conducted by four trained therapists.
Physical Performance was assessed by modified items of the Short Physical Performance Battery  at T1 and T2.
Balance test: balance was tested while participants stood in different positions without any support. The positions were open stance with feet comfortably apart; closed stance with feet side by side; semi tandem stance with feet parallel, heel of one foot touching the big toe of the other foot on the inner side; and tandem stance with feet standing in line, heel of one foot touching the tiptoe of the other foot. Only one attempt was allowed in each position. If a participant was able to hold a position for 10 s, the next most challenging position was tested. The sum of the seconds performed in all positions was calculated, with a maximum of 40 s.
Gait speed: habitual gait speed was timed (stopwatch) over 4 m, with an additional 3 m for acceleration and 3 m for deceleration . The mean gait speed of the two attempts was taken for the analysis [meters per second].
Five-chair rise: performance of sit-to-stand transfer was assessed by timing (stopwatch) five chair rises with the use of arm rests being allowed . Standing up and sitting down five times in habitual speed starting in sitting position and stopping after sitting down the fifth time. The mean time of chair rise performance of the two attempts was taken for the analysis [seconds].
The Barthel Index (BI) was assessed at T1 by the nursing staff responsible for the individual patient according to the “Hamburger Manual” , which was established to increase reliability.
In order to describe the cognitive status of the study sample, the Short Orientation Memory Concentration test  was administered during admission (T1). This test assesses temporal orientation, counting backwards, reciting months backwards and memorizing an address. The range of results varied from 0 (best) to 28 (worst) errors. A score of 11 or more has been shown to indicate possible dementia.
Physical activity was measured with a Physilog ® activity monitor (BioAGM, CH). The device consists of a miniaturized encapsulated data-logger, rechargeable batteries, MMC memory card, triaxial accelerometers (ADXL 202, Analog Device, ±2 g) and single axis gyroscope (ADXRS 150, Analog device, ±200 deg/s). The small size (95 × 60 × 22 mm) and its lightweight (122 g) allowed for attachment on the person’s chest by a harness without hindering the subject.
The analysis algorithm is described elsewhere in detail and has been validated against opto-electronic systems, video and personal observation in different groups of subjects including older adults [28, 30] and patients with Parkinson’s disease . Overall accuracy for the detection of physical activities in older adults was higher than 90 %. For detection of walking, standing, sitting and lying, sensitivity between 87 and 99 %, and specificity between 87 and 99 % is documented. Chest acceleration as well as the chest angular velocity were stored digitally with a sampling frequency of 40 Hz on a MMC memory card.
The participants wore the device during the day (from breakfast until preparing for bed in the evening) for 1 day and were not allowed to take it off except for therapies like massage or radiological investigations. Data used for analysis were restricted to 9 h (9 AM to 6 PM). The reported values in this paper were the cumulative time [minutes] of walking (meaning three consecutive steps or more) (walkcum) and the cumulative time walking and standing (uptime).
The Physical Activity in Inpatient Rehabilitation (PAIR) assessment
Between therapy sessions….
To what extent
1. …I was mostly lying in bed in order to recover
2. …I was mostly sitting in my room in order to recover
3. …I was undertaking little walks on the ward
4. …I was undertaking little walks outside the ward (i.e. cafeteria)
5. … I was undertaking little walks outside the hospital
During inpatient rehabilitation, patients received a standard therapy intervention consisting of physiotherapy and occupational therapy as required. Therapy was offered in group (six to nine sessions per week) and individual (three to eight sessions per week).
Due to non-parametric distribution of some parameters median, minimum, maximum and inter-quartile range were used for descriptive statistics. Wilcoxon-sign-rank test was calculated to show differences between performance at T1 and T2. Criterion-related validity is demonstrated as concurrent and predictive validity . To assess concurrent validity the association between PAIR scores at T1 and T2 and sensor-based cumulative minutes walking and uptime (walking and standing) at T1 and T2 was calculated using Spearman rank correlations . To assess predictive validity, PAIR scores, measured at T1 and T2, were used as independent variables in a linear regression model to predict PA as measured by the Physilog device as a dependant variable. Again, both, cumulative minutes walking and uptime (walking and standing) were considered. The Spearman’s Rho was interpreted according to Domholdt: 0.00–0.25 = little if any correlation, 0.26–0.49 = weak correlation, 0.50–0.69 = moderate correlation, 0.70–0.89 = strong correlation, 0.90–1.00 = very strong correlation . To assess absolute validity Bland-Altman plots were calculated. Because Bland-Altman plots require that the two methods for measuring the same characteristic use the same scale of measurement, a statistically exact conclusion could not be drawn. Yet, we still considered this the best approach to qualify clinically relevant uncertainties across the measurement range .
For the scoring figure, median sensor-based activity values have been calculated for each PAIR score using Microsoft Excel 2007 (Microsoft Corp.). All other analyses were conducted using SPSS version 16 software (SPSS, Chicago, IL, USA).
Barthel index (0–100)
Gait speed [m/s]
5 Chair Rise [s]
Cumulative walking time [h]
Cumulative uptime [h]
Physical activity and physical function at admission and 2 weeks later
2 weeks later
Median (first–third quartile)
Median (first–third quartile)
Gait speed [m/s]
5 Chair Rise [s]
Cumulative walking time [h]
Cumulative uptime [h]
At T2 five patients were not available for the sensor-based PA measurement because of inter-current illness (n = 2), refusal (n = 2), or early discharge (n = 1).
The PAIR questionnaire was administered in approximately 1 to 2 min, whereas the sensor-based PA measurement took approximately 15 min including fixing, taking off and analysis (without the wearing time).
Criterion-related concurrent and predictive validity of the PAIR
Association of the PAIR
Concurrent validity at admission (T1, n = 70)
Walking and standing activity (uptime)
Concurrent validity 2 weeks later (T2, n = 65)
Walking and standing activity (uptime)
PAIR in a regression model
Predictive validity (T1, n = 70)
Walking and standing (uptime)
Predictive validity (T2, n = 65)
Walking and standing (uptime)
Bland-Altman plots revealed slightly heterogeneous pictures at T1 and T2 with overall higher uncertainties in the lower scale range of the PAIR. The plots indicate that the 95 % limits of agreement between the two methods ranged from −2 to +2 points in the PAIR. The two methods, therefore, do not consistently provide similar measures because there is a level of disagreement that includes clinically important discrepancies of up to 2 points (not shown).
The PAIR demonstrated weak concurrent and predictive validity when compared to an objective PA assessment at admission and weak to moderate correlations 2 weeks later in older, ambulatory adults in an inpatient rehabilitation unit. It was easy to administer and its scoring system was valid with exception of the score “six”.
There are several possible reasons for the mostly weak to at best moderate correlations with the sensor-based activity measure. First, the sensor-based system was not only worn between treatment sessions, because it could not be switched off and on before and after the treatment. The PAIR however, specifically asks for activity between therapy units. Second, the device was only worn throughout the day (from 9 am to 6 pm) and participants could have also rated their activity according to what happened before and after this time. In addition, sitting and lying activities as reported for score one and two of the PAIR are not captured by the sensor. It also has to be mentioned that the PAIR could, like other interview-based or proxy-based activity questionnaires, overestimate “real” PA levels . Nevertheless, the correlations were comparable to those that have been reported for much more complex community-based assessments , a satisfying result when taking into consideration the shortness of the assignment.
The scoring system of the PAIR has been confirmed by the current analysis with the exception of the score “six” (“undertaking little walks outside the hospital; yes, sometimes”). This score showed a lower mean cumulative PA than the two scores before (“undertaking little walks outside the ward”). Thus, sometimes leaving the hospital for a walk seems to add less minutes to total cumulative activity, especially when looking at total activity time. This adds to the overall discussion with regard to the accuracy of accelerometers predicting “total PA”  and might reflect specific aspects of the rehabilitation environment that have not been identified in previous analyses in another cohort of inpatients (data not shown). Therefore, we have decided not to change the scoring system of the instrument yet.
With regard to absolute validity, there is evidence of clinically significant differences of ±2 points across the measurement range. However, because of its simple ordinal scale as compared to the continuous scale of the sensors it was not fully appropriate using Bland-Altman plots which require that the two methods for measuring the same characteristic use the same scale of measurement . Still, it has to be acknowledged that further research is necessary to clarify the quality of the PAIR with regard to absolute validity across different inpatient populations.
Since its popularity increased, the validity of body-fixed sensors has been a matter of great debate. Still, recent validation studies often used sensor-based technology as objective standard activity measurements [7, 20]. This might not always be appropriate, because PA can of course be more than just moving the hands (actigraph-sensors) or ambulation (accelerometers, step-counts). Triaxial sensors, mounted to the waist or trunk or armbands that include additional sensors measuring skin temperature, galvanic skin response and heat flux could increase accuracy with regard to energy expenditure but might not fill in the gap of an exact classification of types of activities . However, validity of back- or hip-mounted triaxial accelerometers was very comparable to the mentioned armband in a recent study in patients with chronic obstructive pulmonary disease . In addition, when thinking of geriatric rehabilitation, ambulation is still the most important component of PA and also the primary treatment goal of patients, caregivers and the therapeutic team alike [8, 9]. Therefore, when developing the PAIR the focus was set on walking tasks.
Earlier analyses of the PAIR have demonstrated weak to moderate correlations with physical function assessments, especially if cognition was rated as intact or only slightly impaired : Spearman correlations with gait speed or the Late Life Function and Disability Index ranked r = 0.43–0.53. Together with these earlier results in a different population , the PAIR is the most thoroughly validated PA assessments for inpatients. With regard to the framework by Ainsworth and colleagues the development process of the PAIR can be considered at step six . According to the two main questions in this step, it has to be stressed that the PAIR has been developed to provide a short and valuable PA assessment with the focus on mobility as the most important activity in geriatric rehabilitation rather than to provide a thorough assessment of metabolic equivalents (METs).
When validity results are compared to other new PA assessments in community-dwelling older adults, the PAIR seems to perform slightly better. This could be because of the less complex environment with an increased reminiscence of everyday tasks despite the higher overall cognitive deficit as compared to most other populations used . Conversely, the less favourable outcomes of old and new community PA assessments have been attributed to the highly variable behaviour such as free-living physical activity by self-report .
There are more limitations to consider. Weak to at best moderate correlations pose problems during everyday use, especially when utilized as a screening tool. Significant misclassifications might preclude detailed analyses in especially smaller studies with low effect sizes . Still, as mentioned by Helmerhorst and colleagues  the instruments are always useful on an individual basis and when analyzing PA trajectories in longitudinal projects. For that purpose, the good sensitivity to change of the PAIR, as demonstrated in previous analyses  is of importance. Because of its simplicity, repetitive assessments should not pose an additional problem in clinical practice. Another important limitation of the PAIR is that it has only been validated in patients able to walk at admission. There is no data so far on validity of this instrument in those that are bound to wheelchairs. This problem has been discussed in detail before  and warrants further studies. As demonstrated before, the PAIR also exhibited significant floor and (less) ceiling effects, naturally depending on levels of mobility of the participants studied. Yet, with the focus on inpatients, meaningful activities have been captured and application time, which we consider a very important aspect in a busy clinical practice, has been reduced to a minimum. Still, studies in populations with clearly better or clearly worse functional status (i.e. nursing homes or acute geriatric hospitals respectively) and additional “gold standard” validation techniques (doubly labelled water, diaries, or different sensors) would be helpful to improve relative validity of the PAIR.
To conclude, the PAIR is the currently best validated assessment of PA for (older) inpatients. It is concise and easy to administer. Although its validity is comparable to existing self-rated PA assessments for community-dwelling older adults, relative validity was only weak to moderate and doubts remain with regard to its absolute validity. More studies are warranted to confirm the scoring system and performance of the assessment in different inpatient populations.
Prof. Thorsten Nikolaus, the Director of AGAPLESION Bethesda Clinic Ulm and Chair for Geriatric Medicine at Ulm University, passed away on Sept 26th 2013. He will be warmly remembered and dearly missed by all. The authors would like to thank Aileen Currie for critically reading the manuscript.
Conflict of interest
All authors declare that they have no conflicts of interest.
MDD and the PAIR project were supported by a geriatric research grant to MDD from the Robert Bosch Foundation, Stuttgart, Germany. The MEMBeR study was funded by the Federal Ministry of Labour and Social Affairs (BMAS, Germany (GRR-58663-10/7)). The participants included in this analysis were part of this comprehensive study to evaluate objective and valid measurements assessing indication, duration and success of inpatient rehabilitation after hip fracture and after stroke. The sponsors had no influence on the analysis of data or on the writing of the manuscript.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study
- Ainsworth BE, Caspersen CJ, Matthews CE, Masse LC, Baranowski T, Zhu W (2012) Recommendations to improve the accuracy of estimates of physical activity derived from self report. J Phys Act Health 9(Suppl 1):S76–S84Google Scholar
- Aminian K, Robert P, Buchser EE, Rutschmann B, Hayoz D, Depairon M (1999) Physical activity monitoring based on accelerometry: validation and comparison with video observation. Med Biol Eng Comput 37(3):304–308Google Scholar
- Bandeen-Roche K, Xue QL, Ferrucci L, Walston J, Guralnik JM, Chaves P et al (2006) Phenotype of frailty: characterization in the women’s health and aging studies. J Gerontol A Biol Sci Med Sci 61(3):262–266Google Scholar
- Blair SN, Haskell WL (2006) Objectively measured physical activity and mortality in older adults. JAMA: J Am Med Assoc 296(2):216–218Google Scholar
- Bland JM, Altman DG (2003) Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol 22(1):85–93Google Scholar
- Brown CJ, Friedkin RJ, Inouye SK (2004 Aug) Prevalence and outcomes of low mobility in hospitalized older patients. J Am Geriatr Soc 52(8):1263–1270Google Scholar
- Colbert LH, Matthews CE, Havighurst TC, Kim K, Schoeller DA (2011) Comparative validity of physical activity measures in older adults. Med Sci Sports Exerc 43(5):867–876Google Scholar
- Coronado M, Janssens J-P, de Muralt B, Terrier P, Schutz Y, Fitting J-W (2003) Walking activity measured by accelerometry during respiratory rehabilitation. J Cardiopulm Rehabil 23(5):357–364Google Scholar
- Dakin LE, Gray LC, Peel NM, Salih SA, Cheung VH (2010) Promoting walking amongst older patients in rehabilitation: are accelerometers the answer? J Nutr Health Aging 14(10):863–865Google Scholar
- Denkinger MD (2008) Prevention through physical activity in old age—what is known and what needs to be done? MMW Fortschr Med 150(45):34–37, quiz 38Google Scholar
- Denkinger MD, Coll-Planas L, Jamour M, Nikolaus T (2007) The assessment of physical activity in inpatient rehabilitation—an important aspect of the identification of frailty in hospitalized older people. J Am Geriatr Soc 55(6):967–968, author reply 968–9Google Scholar
- Denkinger MD, Lindemann U, Nicolai S, Igl W, Jamour M, Nikolaus T (2011) Assessing physical activity in inpatient rehabilitation: validity, practicality, and sensitivity to change in the physical activity in inpatient rehabilitation assessment. Arch Phys Med Rehabil 92(12):2012–2017Google Scholar
- Domholdt E (2000) Physical therapy research: principles and applications. Saunders, Philadelphia, PAGoogle Scholar
- Friedl KE (2003) Actigraphy as metabolic ethography: measuring patterns of physical activity and energy expenditure. Diabetes Technol Ther 5(6):1035–1037Google Scholar
- Gebruers N, Vanroy C, Truijen S, Engelborghs S, De Deyn PP (2010 Feb) Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Arch Phys Med Rehabil 91(2):288–297Google Scholar
- Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG et al (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49(2):M85–M94Google Scholar
- Helmerhorst HJF, Brage S, Warren J, Besson H, Ekelund U (2012) A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. Int J Behav Nutr Phys Act 9:103Google Scholar
- Jamour M, Becker C, Bachmann S, de Bruin ED, Grüneberg C, Heckmann J et al (2011) Recommendation of an assessment protocol to describe geriatric inpatient rehabilitation of lower limb mobility based on ICF: an interdisciplinary consensus process. Z Gerontol Geriatr 44(6):429–436Google Scholar
- Jean-Louis G, Mendlowicz MV, Von Gizycki H, Zizi F, Nunes J (1999) Assessment of physical activity and sleep by actigraphy: examination of gender differences. J Womens Health Gend Based Med 8(8):1113–1117Google Scholar
- Jørstad-Stein EC, Hauer K, Becker C, Bonnefoy M, Nakash RA, Skelton DA et al (2005) Suitability of physical activity questionnaires for older adults in fall-prevention trials: a systematic review. J Aging Phys Act 13(4):461–481Google Scholar
- Katzman R, Brown T, Fuld P, Peck A, Schechter R, Schimmel H (1983) Validation of a short orientation-memory-concentration test of cognitive impairment. Am J Psychiatry 140(6):734–739Google Scholar
- Lindemann U (2011) Comment on Bohannon (2011): “Five-repetition sit-to-stand test: usefulness for older patients in a home-care setting”. Percept Mot Skills 113(2):489–490Google Scholar
- Lindemann U, Jamour M, Nicolai SE, Benzinger P, Klenk J, Aminian K et al (2012) Physical activity of moderately impaired elderly stroke patients during rehabilitation. Physiol Meas 33(11):1923–1930Google Scholar
- Lindemann U, Najafi B, Zijlstra W, Hauer K, Muche R, Becker C et al (2008) Distance to achieve steady state walking speed in frail elderly persons. Gait Posture 27(1):91–96Google Scholar
- Lübke N, Meinck M (2008) Current appraisal of external quality assurance procedures in geriatric rehabilitation. Rehabilitation (Stuttg) 47(1):39–48Google Scholar
- Mackey DC, Manini TM, Schoeller DA, Koster A, Glynn NW, Goodpaster BH et al (2011) Validation of an armband to measure daily energy expenditure in older adults. J Gerontol A Biol Sci Med Sci 66A(10):1108–1113Google Scholar
- Murphy SL (2009) Review of physical activity measurement using accelerometers in older adults: considerations for research design and conduct. Prev Med 48(2):108–114Google Scholar
- Najafi B, Aminian K, Paraschiv-Ionescu A, Loew F, Büla CJ, Robert P (2003) Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans Biomed Eng 50(6):711–723Google Scholar
- Nelson ME, Rejeski WJ, Blair SN, Duncan PW, Judge JO, King AC et al (2007) Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 39(8):1435–1445Google Scholar
- Paraschiv-Ionescu A, Buchser EE, Rutschmann B, Najafi B, Aminian K (2004) Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation. Gait Posture 20(2):113–125Google Scholar
- Patterson F, Blair V, Currie A, Reid W (2005) An investigation into activity levels of older people on a rehabilitation ward: an observational study. Physiotherapy 91(1):28–34Google Scholar
- Purser JL, Kuchibhatla MN, Fillenbaum GG, Harding T, Peterson ED, Alexander KP (2006) Identifying frailty in hospitalized older adults with significant coronary artery disease. J Am Geriatr Soc 54(11):1674–1681Google Scholar
- Salarian A, Russmann H, Vingerhoets FJG, Burkhard PR, Aminian K (2007) Ambulatory monitoring of physical activities in patients with Parkinson’s disease. IEEE Trans Biomed Eng 54(12):2296–2299Google Scholar
- Schutz Y, Weinsier RL, Hunter GR (2001) Assessment of free-living physical activity in humans: an overview of currently available and proposed new measures. Obes Res 9(6):368–379Google Scholar
- Shephard RJ (2003) Limits to the measurement of habitual physical activity by questionnaires. Br J Sports Med 37(3):197–206, discussion 206Google Scholar
- Stucki G, Ewert T, Cieza A (2002) Value and application of the ICF in rehabilitation medicine. Disabil Rehabil 24(17):932–938Google Scholar
- Talkowski JB, Lenze EJ, Munin MC, Harrison C, Brach JS (2009) Patient participation and physical activity during rehabilitation and future functional outcomes in patients after hip fracture. Arch Phys Med Rehabil 90(4):618–622Google Scholar
- Trochim W, Donnelly JP (2006) The research methods knowledge base. 3rd ed. Atomic DogGoogle Scholar
- Van Remoortel H, Raste Y, Louvaris Z, Giavedoni S, Burtin C, Langer D et al (2012) Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry. PLoS ONE 7(6):e39198Google Scholar
- Wissler C (1905) The Spearman correlation formula. Science 22(558):309–311Google Scholar
- Zisberg A, Shadmi E, Sinoff G, Gur-Yaish N, Srulovici E, Admi H (2011) Low mobility during hospitalization and functional decline in older adults. J Am Geriatr Soc 59(2):266–273Google Scholar