
==== Front
Ann Med
Ann Med
Annals of Medicine
0785-3890
1365-2060
Taylor & Francis

39239877
10.1080/07853890.2024.2399963
2399963
Version of Record
Review Article
Physical Medicine & Rehabilitation
Monitoring postures and motions of hospitalized patients using sensor technology: a scoping review
M. L. Becker et al.
Becker Marlissa L. a
Hurkmans Henri L. a
Verhaar Jan A.N. b
Bussmann Johannes B.J. c
a Department of Orthopaedics and Sports Medicine - Physical Therapy, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
b Department of Orthopaedics and Sports Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
c Department of Rehabilitation Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07853890.2024.2399963.

CONTACT Marlissa Becker m.becker@erasmusmc.nl Erasmus MC University Medical Center Rotterdam, Department of Orthopaedics and Sports Medicine - Physical Therapy, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands
6 9 2024
2024
6 9 2024
56 1 23999637 8 2023
15 7 2024
17 7 2024
KnowledgeWorks Global Ltd.5 9 2024
published online in a building issue5 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Background

Sensor technology could provide solutions to monitor postures and motions and to help hospital patients reach their rehabilitation goals with minimal supervision. Synthesized information on device applications and methodology is lacking.

Objectives

The purpose of this scoping review was to provide an overview of device applications and methodological approaches to monitor postures and motions in hospitalized patients using sensor technology.

Methods

A systematic search of Embase, Medline, Web of Science and Google Scholar was completed in February 2023 and updated in March 2024. Included studies described populations of hospitalized adults with short admission periods and interventions that use sensor technology to objectively monitor postures and motions. Study selection was performed by two authors independently of each other. Data extraction and narrative analysis focused on the applications and methodological approaches of included articles using a personalized standard form to extract information on device, measurement and analysis characteristics of included studies and analyse frequencies and usage.

Results

A total of 15.032 articles were found and 49 articles met the inclusion criteria. Devices were most often applied in older adults (n = 14), patients awaiting or after surgery (n = 14), and stroke (n = 6). The main goals were gaining insight into patient physical behavioural patterns (n = 19) and investigating physical behaviour in relation to other parameters such as muscle strength or hospital length of stay (n = 18). The studies had heterogeneous study designs and lacked completeness in reporting on device settings, data analysis, and algorithms. Information on device settings, data analysis, and algorithms was poorly reported.

Conclusions

Studies on monitoring postures and motions are heterogeneous in their population, applications and methodological approaches. More uniformity and transparency in methodology and study reporting would improve reproducibility, interpretation and generalization of results. Clear guidelines for reporting and the collection and sharing of raw data would benefit the field by enabling study comparison and reproduction.

KEY MESSAGES

In a clinical setting, wearables are currently used to monitor postures and motions in a wide variety of study applications and hospital populations.

Measurement of postures and motions in the hospital setting is characterized by methodological heterogeneity. This poses a significant challenge, impacting the interpretation of results and hindering meaningful comparisons between studies

Following guidelines for reporting and the collection and sharing of raw data would benefit the field.

Keywords

Accelerometry
Exercise
Hospital
Physical Behaviour
Rehabilitation
Wearables
No funding was received.
==== Body
pmcIntroduction

The dangers of physical inactivity for hospitalized patients are clear and evident; adverse outcomes, functional decline, increased disability and mortality [1–4]. Clinical physical therapists and nurses play a key role in early mobilization and stimulating patients’ physical activity. Although nobody disputes the dangers of bed rest, older hospital patients are still sedentary more than 80% of the time, even if they are capable of independent mobility [1].

Patients and nursing staff report the presence of physical therapists, encouragement, and assistance from others as critical enablers for improving physical activity during rehabilitation [5]. The need for hospital staff supervision and attention is clear, but they have limited time and resources to continuously monitor or support patients’ physical behaviour and mobility. The availability of professional staff during rehabilitation will probably decrease further in the future [6].

The failings of the current situation, combined with the future challenges, mean that other solutions must be found to stimulate physical activity in hospital patients. There is evidence that objective feedback from wearables can improve physical activity levels [7–9]. Wearables usually consist of small, light, wireless, non-invasive sensors attached to the body, used for various applications, such as remote health and/or safety monitoring, (home) rehabilitation, or treatment evaluations. This technology has already been used to monitor three dimensions of physical behaviour: assessing levels or intensity of physical activity, types of postures and motions, or sleeping behaviours [10,11].

Wearables have traditionally focused on measuring the intensity of physical activity because, in many studies, health outcomes such as cardiovascular health, fitness, the incidence of diseases, and mortality are the primary outcomes of interest [12]. However, in hospital care the recovery of physical functioning, independence, and a fast discharge are more relevant. Clinical physical therapy focuses on rehabilitation and mobilization by monitoring specific body posture and motion-related outcomes, such as the duration of standing and walking, the number of transfers or steps taken.

There is a growing body of evidence looking at the utilization of wearable devices for monitoring postures and motions in hospitalized patients. However, synthesized information on the applications and the methodological approaches to perform these measurements is lacking. As a result, for researchers and clinicians it is difficult to determine which devices, settings and methodology are most suitable for their aims and measurements. To the best of our knowledge, no previous review has specifically addressed these issues within the context of hospitalized patients. A narrative analysis of the current literature will fill this knowledge gap. Our study therefore aims to review the current literature on the device applications and methodological approaches, focusing on monitoring postures and motions in hospitalised populations.

Materials and methods

Study design

This scoping review followed a systematic approach to synthesize existing literature on the applications and methodologies of monitoring postures and motions in hospitalized patients. The methodology of this review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline and the framework by Arksey & O’Malley [13] and Levac et al. [14]. No protocol was registered for this review.

Search strategy

A comprehensive and systematic search of the electronic databases of Embase, Medline (Ovid), Web of Science, and the first 200 results of Google Scholar was completed to identify relevant articles published from 2010 to February 2023, and updated in March 2024. The search was assisted by a hospital librarian, who developed the search strategies, downloaded the results, and deleted duplicates. The search keywords included hospitalized patients, physical behaviour, objective sensor technology and synonyms. The entire search strategy, specified for each database, can be found in Supplementary Material A1.

Eligibility criteria

Studies were considered eligible for inclusion when they met the following criteria: (1) an adult population (≥18 years) admitted to a medical care hospital; (2) an admission period shorter than 14 days; (3) any wearable or non-wearable sensor technology used to monitor postures and movements objectively.

Studies were not eligible if they included (1) patients admitted to long-term rehabilitation facilities; (2) patients admitted to a psychiatric hospital (wards); (3) patients with mental health disorders; (4) study protocols, review articles, validation studies, editorials, short communications, abstract or posters; (5) if the full text was unavailable in English.

Study selection

Articles from 2010 until February 2023 were included. Two reviewers (HH and MB) independently assessed the eligibility of titles and abstracts using the Endnote X7 software as described by Bramer [15]. In the next step, the full-text articles meeting the inclusion criteria were read independently to determine eligibility. Any disagreements were resolved by discussion. Reviewers were not blinded to the author or journal information during this selection process.

Data extraction

The lead author (MB) extracted information from the articles using a personalized standard form. Data were extracted from the methods sections using three categories: (1) device, (2) measurement and (3) analysis characteristics. Device characteristics included specifications on the type of sensor, number of sensor units, placement and outcomes. Measurement characteristics included choices regarding attachment methods, wear time and measurement durations. Analysis characteristics contained all relevant information on data reduction and processing from raw accelerations to meaningful outcomes, like non-wear analysis, device setting of epochs, sample frequencies, and software tools. Additional information could be collected from appendices or direct references. Information on the study and participants was also collected. Finally, additional information on waterproof devices, donning and doffing, registration diaries, range of gravity, raw device outcomes, data transfers, battery and storage capacities, processing method in software tools, and specific in-hospital results were obtained if reported in the included articles. This information was not reported in this review if the reporting rates were insufficient.

Data analysis

After data extraction, a narrative analysis was performed to report the frequencies of use and settings for device, measurement and analysis characteristics.

Results

The literature search returned 15.032 articles, resulting in 49 articles that met the inclusion criteria. The reasons for the exclusions can be found in the PRISMA-ScR flow chart in Figure 1.

Figure 1. PRISMA-ScR flow chart of study selection.

Study characteristics and device applications

Forty studies had an observational study design, and the remaining studies had an experimental (n = 6, 12%) or feasibility (n = 3, 6%) design. Most studies were published in the last five years (n = 30, 61%), and the others from 2012 to 2018. The most common study aims were to gain insight into patient status regarding behavioural patterns (n = 19, 39%), to investigate physical behaviour in relation to other parameters such as muscle strength or length of stay (n = 18, 37%), to examine the effect of interventions (n = 7, 14%), or to examine new measurement methods (n = 5, 10%). Studies with interventions were not more common in the last five years than other study aims.

Patient characteristics

The patient populations were specified in 98% of the studies. The studies were on older adults (n = 14, 29%), patients awaiting surgery or patients post-surgery (n = 14, 29%), acute or sub-acute stroke (n = 6, 12%), intensive care admissions (n = 4, 8%), pulmonary ward admissions (n = 3, 6%), internal medicine admissions (n = 3, 6%), COPD exacerbations (n = 2, 4%), cardiology admissions (n = 2, 4%), antenatal department (n = 1, 2%), or patients with upper limb injury (n = 1, 2%). One study did not further specify the hospitalized population [16]. The number of participants per study ranged from 1 to 8653. Studies reported their mean age (n = 35, 71%), ranging from 53 to 86 years, or the reported median age (n = 14, 29%) from 30 to 85 years. Other study and participant characteristics can be found in Table 1. The included study populations are also represented in Figure 2(a).

Figure 2. a) Included patient populations b) included use of single/multiple devices c) included device wear positions d) included device outcomes.

Table 1. Study and participant characteristics.

Study, year	Study design	Goal	Population (n; mean age (SD) [years]; male [%])	Measurement periods in study (n)	Results	Conclusion	
Askim et al. (2013) [58]	Prospective cohort	To investigate how PA changes over the first 6 months after stroke and how activity is related to function.	Stroke patients (n = 28; 78.7(8.7); 53.6)	4: T1 < 14d post-stroke T2 1-month follow-up T3 3-month follow-up T4 6-month follow-up	Time spent lying and upright – adjusted for age/stroke severity – significantly changed with-134.8min and +65.7 min respectively between T1 and T2.	Activity levels are very low during the first six months after stroke. Time in an upright position increased as function improved.	
Baldwin et al. (2020) [57]	Prospective cohort	Examine sedentary and activity patterns during ICU recovery and associations with physical function, muscle strength, and length of stay.	ICU patients (n = 40; 62(15); 60)	3: T1 awakening ICU T2 discharge ICU T3 up to 2 days before hospital discharge	There was a significant mean decrease in time spent lying/sitting of −0.5% (T1-T2), −2.5% (T2-T3), and −3.0% (T1-T3), and time spent upright significantly increased with 7.0 (T1-T2), 36.0 (T2-T3) and 43.0 (T1-T3) min.	ICU survivors transition from high sedentary behaviour to low-intensity activity over acute hospitalization.	
Bendix et al. (2023) [31]	Prospective cohort	To assess physical resting in pregnant women with(out) activity restrictions and to explore the relationship with admission status.	Pregnant women in and outpatients, with or without activity restrictions (n = 72; median 30.5 (22-43); 0)	1: T1 7 days in/outpatient	Inpatients spent 17.04h/day resting and 5.95 h/day sitting upright, with 0.38h/day standing upright and 0.22h/day walking.	Hospital admission did not influence activity restriction adherence. The type of activity restriction did not influence the physical resting positions or step count.	
Borges & Carvalho (2012) [56]	Prospective cohort	To investigate the effect of hospitalization due to acute exacerbation of PADL in COPD patients and to evaluate factors that determine the PA during and after hospitalization.	COPD exacerbation (n = 20; 68.6(10.7); 70)	2: T1 3-4th day hospitalization T2 1-month follow-up	Significant changes were observed between hospitalization and 1 month after discharge for inactive time (86.7 to 69.6%), lying (57.6 to 30.7%), active time (13.3 to 30.4), standing (11.8 to 22.7%), and walking (1.0 to 6.1%). Changes in sitting time (29.1 to 38.9) were not significant.	Patients are inactive during hospitalization but become more active 1 month after discharge. Previously hospitalized are more inactive both during and after the exacerbation.	
Borges et al. (2015) [55]	Prospective cohort	To quantify the PA in daily life, muscle strength, and exercise capacity in short and medium term in survivors of severe sepsis and septic shock.	Sepsis ICU patients (n = 72; 53.4(17.6); 50)	2: T1 wards after ICU discharge T2 3-month follow-up	Septic patients spent most of their time lying or sitting (89.2%), which significantly reduced to 58% after 3 months. Septic patients showed a significantly lower walking time per day compared with healthy subjects in the ward (1.9 ± 1.6% vs 10.1 ± 4.4% and 3 months later (6.3 ± 3.0% vs 10.1 ± 4.4%).	Survivors of sepsis admitted to the ICU have a substantial reduction in PA, exercise capacity, and muscle strength compared to healthy subjects that persist even 3 months after hospital discharge.	
Conijn et al. (2020) [54]	Quasi-experimental pilot	To evaluate a multicomponent intervention’s feasibility and preliminary effects to decrease sedentary time during hospitalization.	Elective organ transplantation or vascular surgery (Intervention n = 52; 57.7(15.0); 52), Control(n = 42; 59.1(13.0); 62))	2: T1 during hospitalization (except IC) T2 one week after discharge	Median sedentary time decreased from 99.6% to 95.7% and 99.3% to 91.0% between days 1 and 6 in the control and intervention groups, respectively. On day 6, sedentary time was significantly lower in the intervention group, with a difference of 41 min.	Intervention is feasible and may be effective.	
Dall et al. (2019) [53]	Randomized cross-over	To examine the effect of PA measurements with visual feedback on the activity level on the amount of PA.	Pulmonary patients (Intervention n = 45; 73.8(12.8); 51.1; Control n = 48; 71.9(13.6); 47.9)	1: T1 asap hospital admission until discharge/ transfer or max 7 days	Across all patients (both feedback and control), there were no statistically significant changes in the measures of physical activity.	The intervention was not effective, except for a small subgroup.	
Davenport et al. (2015) [52]	Observational	To determine PA levels during acute inpatient admission following hip fracture surgery.	Post-surgery orthopaedic patients (n = 20; 79.1(9.3); 10)	1: T1 study admission until discharge or max 7 days	PA levels were extremely low, with participants spending an average of 99% of the day lying or sitting. No measures of PA were significantly associated with length of stay.	PA levels are very low.	
de Oliveira et al. (2021) [63]	Prospective cohort	To assess the level of PADL and isometric muscle strength of the quadriceps in patients hospitalised for COPD exacerbation and to verify changes after three months of hospital discharge.	COPD exacerbation (n = 24; 66(7.61); 46)	1: T1 < 24h until day 5 or discharge T2 30 days after discharge T3 3 months after discharge	Inactive time significantly reduced 30 days after hospital discharge. Significant increase in active time after 3 months, but not 30 days after discharge.	The level of PA showed significant improvement due to the increase in active time and the number of steps after three months of hospital discharge and to the reduction of inactive time 30 days after hospital discharge.	
Evensen et al. (2017) [51]	Observational	To describe patients and their level of PA and to explore the association with physical function, age, diagnosis of cognitive impairment, function of personal ADL, and comorbidity.	Older adults (n = 38; 82.9(6.3); 31.6)	1: T1 asap after admission until discharge	Mean upright time one day early after admission was 1171.1 min. There was a significant positive association between upright time and physical function.	Participants’ mean time in upright position one day early after admission was almost two hours, indicating a high level of PA compared to results from similar studies.	
Floegel et al. (2018) [50]	Prospective cohort	To investigate the predictive value of posture and ambulation during a hospital stay.	Older adults (n = 27; 78.0(9.8); 48.2)	1: T1 study admission until the day after discharge	Participants spent 63.0 % of their hospital time lying down, 30.2% sitting, 5.3% standing, and 1.9% ambulating. Each 10% increase in lying time associated with 0.7s longer TUG time.	Older adults with heart failure were sedentary during hospitalization.	
Floegel et al. (2022) [30]	Prospective cohort	To examine the use of accelerometry to obtain postures and STS metrics of hospitalized cardiac patients.	Older adults with heart failure (n = 27; 78(9.8); 48.2)	1: T1 < 24h admission to discharge	Patients spent their time 60.3% lying, 20.3% sitting, 5.3% standing, and 2% stepping.	Accelerometry supports mobility assessments with continuous, objective data.	
Fuchita et al. (2023) [24]	Observational	To evaluate accuracy of mobility scale.	Abdominal surgery patients (n = 56; 64 (10); 45%)	1: T1 < 2h Post-operative	Patients spent 97.7% of their time lying or sitting.	Mobility scale did not accurately detect activities.	
Gilmore et al. (2020) [49]	Prospective cohort	To describe PA patterns of patients in the first week after lumbar spinal surgery.	Post-surgery lumbar patients (n = 216; 62(13.9); 49)	1: T1 morning after surgery until day 7	Daily sedentary time decreased from 93% on day 1 to 84% on day 6. Walking time increased from 17 min to 54 min, and standing increased from 87 min to 174 min.	Patients walk less than an hour daily over the first week after lumbar surgery.	
Halfwerk et al. (2021) [62]	Prospective cohort	To select and evaluate accelerometers for objective qualification of in-hospital mobilization after cardiac surgery.	Post-surgery cardiac patients (n = 29; median 70 (64-74); 75.9)	1: T1 until day 7 or discharge	Patients significantly decreased their time in bed each day by 41 min and significantly increased their time spent standing by 5.7 min a day, walking time by 2.0 min a day, and walking the stair by 0.68 min a day. No significant findings for minutes sitting or cycling.	The presented approach is applicable for measuring all six activities and monitoring postoperative recovery.	
Hartley et al. (2018) [47]	Clinical feasibility	Using two accelerometers to discriminate lying, sitting, standing, and moving in older hospitalized patients.	Older adults (n = 24; median 80.5 range (70-95.0); 50)	1: T1 48h during hospital admission	Patients were observed to spend 61.2% of the recording time lying, 35.6% sitting, 2.1% standing, and 1.1% standing and moving.	This methodology can differentiate between activities and is acceptable from a hospitalized older person’s perspective.	
Hartley et al. (2020) [48]	Prospective cohort	To investigate clinical predictors of in-hospital activity during the first 24h of hospital admission in older adults.	Older adults (n = 62; median 85.0 IQR (80.2-87.0); 58.1)	1: T1 first 24h hospital admission until day 7 or discharge	Patients spent a median active time of 0.5h standing or walking in the first 24 hours.	PA, particularly in the acute phase of hospitalization, is very low in older adults.	
Harvey et al. (2018) [46]	Case Report	To present the measurement of sedentary time before the incident, during hospitalization, and post-discharge.	Patient with upper limb injury (n = 1; 72; 0)	1: T1 longer measurement period of which 2 days spent in hospital	Considerable sedentary time is observed during the acute hospital stay (98.6%).	The report demonstrated detrimental consequences of an upper limb injury to the activity levels of an older adult receiving hospital care.	
Haslam-Larmer et al. (2021) [61]	Mixed methods embedded case	To describe early mobility activities post-surgery and identify factors influencing participation in early mobility activities.	Older post-surgery hip fracture patients (n = 19; 83.2(10.5); 26)	1: T1 until day 10 or discharge	The activity monitor demonstrated a mean sedentary time of 23.18h and a median upright team of 24 min.	There are high sedentary times after surgical repair for fragility hip fracture.	
Howie-Esquivel & Zaharias (2013) [45]	Prospective cohort pilot	To describe mobility, active time, physical function, and skin condition of heart failure patients.	Heart failure patients (n = 32; 58.2(13.6); 78.1)	1: T1 < 48h of hospital admission until day 5 or discharge	During hospitalization, 70% (16.8h) was spent lying in bed. The average time spent standing or walking was 4.1% (59 min) daily.	Despite baseline ambulatory status, almost all time is spent in bed.	
Jawad et al. (2022) [29]	Prospective cohort	To assess PB during hospitalization and compare it to PB after discharge.	Older adults (n = 80; median 80.9 IQR 75-88; 32)	3: T1 inclusion till discharge T2 1 week after discharge T3: 1 week, four weeks after discharge	During hospitalization, the median upright time was 1.7h, 21.4h was spent sedentary.	Hospitalized older adults spent most time sedentary, with activity peaks in the morning.	
Jeldi et al. (2016) [44]	Prospective cohort	To gain quantitative insight into in-hospital mobilization.	Post-surgery THA orthopaedic patients (n = 44; median 68 IQR 9 range[50-82]; 29.5)	1: T1 < 4h return to ward until discharge	Some participants performed no activity in the first 24h after surgery with a median upright time of 25 min. However, in the last 24h before discharge, participants spent a median of 134 min upright.	Although there was considerable activity within rehabilitation periods, most STS and upright time occurred outside rehabilitation.	
Jones et al. (2024) [20]	Feasibility, prospective cohort	To evaluate feasibility of accelerometer.	Critical care unit (n = 12; median 56.67 (IQR 14.33); 25%)	1: T1 < 48h admission	Participants wore the device for a median time of 268.35 hours, 99.58% of time.	Collecting PA data with thigh-worn accelerometer is feasible in critical care units.	
Karlsen et al. (2017) [43]	Prospective cohort	To provide a more detailed insight into the changes in strength and functional performance in older hospitalised medical patients.	Older adults (n = 151; 85.2(7.2); 25.8)	1: T1 until discharge	No difference was observed in the time spent being inactive between the first and last day (91.2 ± 8.0% vs 91.5 ± 7.1%).	Functional performance of the lower extremities in geriatric patients improves moderately over a hospital stay of less than 14 days, with more considerable improvement in patients with high activity levels.	
Katz et al. (2022) [28]	Prospective observational	To describe the PA of admitted stroke patients and associate them with clinical status.	Adults with ischemic stroke (n = 2; 69.4(33.4); 62)	1: T1 < 24h admission until 2 weeks or discharge	Overall activity was low. Both upright and sitting times were increased in morning shifts compared to evening shifts.	Patients mainly performed out-of-bed activity during the morning shifts, as upright sitting.	
Kawamura et al. (2019) [42]	Prospective cohort	To clarify the PA levels and characteristics of older patients with pneumonia.	Older adults with pneumonia (n = 29; median 85.0 IQR (78.0-89.0); 69.0)	1: T1 < 48h hospital admission for 7 days	The median time spent in an upright position and walking was significantly less for the pneumonia group (320.0 and 3.8 min) than for the community-dwelling group (729.0 and 71.0 min). In the pneumonia group, times spent upright and walking did not increase significantly during the study period.	Time spent in the upright position and walking among older patients with pneumonia did not increase, despite the gradual improvement of the disease.	
Kerr et al. (2016) [41]	Prospective cohort	To quantify the PA behaviour of stroke survivors discharged from the acute hospital setting to community living with early supported discharge.	Stroke patients (n = 41; 69(11); 46.3)	2: T1 in hospital T2 week after discharge	During hospitalization, participants spent a median of 95.9% of the day (1381 min) in sedentary positions, 3.5% (51 min) standing, and 0.7% (11 min) walking.	Community living with early supported discharge promoted higher levels of PA in medically stable stroke survivors.	
Kersten et al. (2023) [17]	Prospective cohort	To record activity and detect parameters for knee function.	Post-surgery total knee arthroplasty patients (n = 20; 69.9 (8.8); 45%)	1: T1 Day 1 post-operation to discharge	Patients percentage of immobilization during daytime was 91.2% on day one and still 69.9% on the last day.	IMUs measure activity postoperatively well, a wide range of motion patterns was observed.	
Kirk et al. (2022) [85]	Prospective observational	To quantify patient PA and determine the relationship with LOS.	Adult with elective lower limb arthroplasty surgery (n = 74; 67(10); 47)	1: T1 post-surgery to discharge	Participants performed, on average, 52 minutes of upright activity a day. All PA variables were negatively associated with LOS.	Lower levels of PA on postoperative day 2 were associated with longer LOS.	
Koenders et al. (2019) [40]	Retrospective, secondary analysis, feasibility	To examine the feasibility of inpatient monitoring of PA with a wearable sensor and to analyse the PA of patients during hospital admission in relation to barriers of PA.	Internal medicine and surgical patients (n = 39; 54(15); 72)	1: T1 < 24h hospital admission or post-surgery until day 4 or discharge	Physical activity differed substantially between patients and throughout their hospital stay. Patients were lying for a median of 12.1h, sitting/standing 11.8h, and walking 0.1h daily. Time lying is related significantly to pain levels and drain use.	During hospital stay, patients spend most of their time lying in bed.	
Kramer et al. (2013) [39]	Prospective cohort pilot	To quantify the PA levels of individuals with stroke in an acute stroke ward and to determine if PA levels change within the first month after stroke.	Stroke patients (n = 16; median 79.5 IQR (62.5-85); 38)	2: T1 < 14d post-stroke T2 4 weeks post-stroke	There were no significant changes in transitions or times spent in dynamic activity, lying, and sitting.	Activity levels were low at an acute stroke ward and did not significantly change within the first month.	
Kronborg et al. (2016) [38]	Observational	To objectively measure the PA the first week after hip surgery and its relation to functional performance and fear of falling at discharge.	Post-surgery hip fracture patients (n = 37; 80(8.4); 22)	1: T1 day 1-3 post-surgery for 10 days	Upright time increased from a median of 13 min at day 2, to 46 min at day 7.	More upright time at discharge was associated with less fear of falling, fast gait speed, and a faster TUG.	
Lehmkuhl et al. (2023) [23]	Prospective cohort	To describe objective PA in ventilated ICU patients.	Ventilated ICU patients (n = 39; med 69 (IQR 62-77); 51%)	1:T1 ICU admission till ICU discharge	Patients spent 20/24h lying, 3h sitting and 1h standing/moving or cycling.	ICU patients were primarily sedentary.	
Mavroeidi et al. (2019) [16]	Observational	To assess whether getting up and dressed or having a personalized "mobility plan" while in hospital reduces SB and increases movement in ward-based patients.	Hospital patients (n = 43; 83.8(8.3); 40)	1: T1 until day 4	There were no significant differences between the 4 groups (control, education, #endpjparalysis, and personalized activity passports) for upright and sedentary time.	Sequential initiatives within a ward setting to reduce SB were unsuccessful.	
Nakanishi & Goto (2023) [18]	Clinical feasibility	To determine whether machine learning can capture PB changes.	Thoracoscopic surgery patients (n = 2; 70.5 (0.5); 100%)	2: T1 Preoperative T2 3-4 days Post-operative	Patients activity level was near preoperative values on day 4 (patient 1) or day 2 (patient 2).	Machine learning model effectively predicted actions of surgical patients with high accuracy.	
Norvang et al. (2018) [37]	Prospective cohort	To describe the amount of time spent in lying, sitting, and upright positions early after stroke and how these activity levels change during the hospital stay.	Stroke patients (n = 58; 75.1(12.0); 45.5)	1: T1 < 7d post stroke until day 7 or discharge	Time spent sitting and time spent upright significantly increased per day during hospitalization by 22.10 and 3.75 min, respectively.	Patients increased their daily sitting and upright time during the initial hospital stay after stroke.	
Oestergaard et al. (2018) [19]	Non-randomized clinical Trial	To investigate the effect of mobility and muscle strength with exercise program.	Geriatric patients (Intervention (n = 152; 86 (7.2); 30.3%); Control (n = 151; 85.2 (7.2); 25.8%)	2: T1 Pre-operation T2 Post operation until discharge	No between-group difference for mobility and muscle strength, but shorter length of stay for intervention group.	Physical activity shortens length of stay and can improve function.	
Pedersen et al. (2013) [36]	Prospective cohort	To quantify 24-hour mobility during hospitalization in acutely admitted older medical patients and to assess their daily level of mobility.	Older medical inpatients Ambulatory (n = 42; 84.7 (78.6-87.2); 55)Non-ambulatory (n = 6; 82.8 (79.9-88.0); 33)	1: T1 < 48h until day 10 or discharge	Compared to non-ambulatory patients, ambulatory patients were significantly lying in bed less (17.0 vs 22.6h), sitting more (5.1 vs 1.0h), and spent more time upright (1.1 vs 0.2h).	Older acutely hospitalised medical patients with walking ability spent 17h/d of their in-hospital time in bed, and the level of in-hospital mobility seems to depend on the patient’s mobility.	
Piotrowicz et al. (2023) [22]	Cross-sectional	To check influence of respiratory infection on PA.	Older patients internal medicine and geriatrics (n = 31; 79.0; 42%)	2: T1 < 48h after admissionT2 < 48h before discharge	Daily time spent sitting or reclining was 23.7 upon admission and 23.5 at discharge.	No difference between patients groups.	
Porserud et al. (2019) [35]	Non-randomized controlled trial	To evaluate the activity board as a standardized method to enhance mobilization and postoperative recovery after abdominal cancer surgery.	Post-surgery abdominal cancer patients Intervention (n = 67; 69.3(11.4); 52.2) Control (n = 66; 67.0 ± 13.1; 48.5)	1: T1 asap postoperative until day 5 or discharge	Post-surgery patients with activity board showed significantly less time lying (1062 vs 1140) and significantly more time upright (78 vs 42), standing (60 vs 42), and walking (18 vs 6) than the standard treatment group.	The activity board is an effective tool to enhance mobilization after abdominal surgery due to cancer in hospital settings and could lead to improved postoperative recovery.	
Ramsey et al. (2021) [60]	Observational matched cohort	To compare objectively measured physical activity and sedentary behaviour in geriatric rehabilitation inpatients receiving care in the home-based setting vs the hospital-based setting.	Older adults (n = 141; 82.9(7.8); 42.6)Of which matched (n = 18; 80.1(7.4); 61.1)versus home-based inpatient rehabilitation (n = 18; 81.9(8.6); 61.1)	1: T1 until day 7	Median physical activity measures were consistently higher in home-based patients than in hospital-based patients.	Home-based geriatric inpatient rehabilitation is associated with greater physical activity compared with the hospital-based setting, even after matching for sex and physical function.	
Rojer et al. (2021) [59]	Prospective cohort	To identify determinants of instrumented sedentary behaviour and instrumented physical activity in geriatric rehabilitation inpatients considering five major geriatric domains.	Older adults (n = 145; 83.0(7.7); 44.1)	1: T1 until day 7 or discharge	A higher FAC score was significantly associated with higher sitting time, lower lying time, and lower non-upright time. Higher gait speed and SPPB score were significantly associated with lower non-upright time.	In geriatric rehabilitation inpatients, worse morbidity, depressive symptoms, worse physical and functional performance, and worse nutritional status were associated with higher sedentary behaviour and lower physical activity.	
Sheedy et al. (2020) [34]	Cross-sectional	To describe the PA patterns of patients with acute stroke during hospitalization and to examine the relationship between the PA behaviour of patients with stroke and their stroke severity.	Stroke patients (n = 78; median 80.5 IQR (70-86); 53)	1: T1 < 48h until day 14 or discharge	Participants spent a median of 98% of the day inactive, with only 1%(18 min) standing and less than a minute walking or stepping.	The ActivPal device was feasible to use in an acute stroke setting.	
Theou et al. (2019) [33]	Prospective cohort	To examine how long and how frequently older hospitalised patients spent upright, whether duration and frequency of upright time change by time of day, day of the week, and during hospitalization, and whether these relationships differ based on the mobility level.	Older adults (n = 111; 82.2(8); 48)	1: T1 < 48h until day 14 or discharge	On average, participants were upright for 54.9 min during awake hours. The independent group had a significantly higher upright time than the bedridden or person-assisted groups (151.1 vs 25.0 vs 29.5). Upright time significantly decreased with 4.5 min a day for the independent group but significantly increased with 2.4 and 3.6 min for the bedridden and person-assisted groups.	Hospitalised older adults spend only 6% of their awake hours upright.	
Turan et al. (2023) [25]	Retrospective observational	To evaluate association between mobilization and complications and length of stay.	Elective surgery (n = 8653; 57.6 (16.0); 47.6%)	1: T1 < 36 postoperatively	Mobilization time was median of 3.9 minutes per monitored hour.	Mobilization was associated with fewer postoperative complication and shorter stay.	
Van Dijk-Huisman (2023) [21]	RCT	To investigate effectiveness of Hospital Fit on PA.	Pulmonology/ internal medicine patients (Intervention n = 39; med 64 (IQR 56-69); 59%), Control (n = 39; med 62 (IQR 55-67), 54%).	1	Hospital Fit use resulted in 27.4 min extra standing/walking on day 5.	Hospital Fit appears valuable in increasing PA.	
Villumsen et al. (2015) [32]	Prospective cohort	To investigate the time spent walking and the development in time spent walking during the hospitalization of geriatric patients.	Older adults (n = 100; median 84 IQR (80-88); n.r.)	1: T1 day 3 until discharge	The median time spent walking was 7 min per day, and time upright was 83 min a day. Time spent walking significantly increased from a median of 4 min a day to 10 min a day.	When walking only 7 min per day, patients could be classified as inactive and at risk for functional decline; nonetheless, the PA level increased significantly during hospitalization.	
Woo et al. (2021) [26]	Prospective observational	To investigate early PA time and association with the recovery of ADL.	Older patients with community-acquired pneumonia (n = 87; median 82 (75-89); 53)	1: T1 day 3-9 of hospital admission	Median PA time was 69 min/day.	Increasing early walking time might be an effective strategy to improve ADL recovery.	
PB: physical behaviour, T1: first measurement period; T2: second measurement period.

Device characteristics

The studies measured their outcomes using single (n = 36, 73%) or multiple (n = 13, 27%) accelerometers and 15 different device brands were used. Commonly reported outcomes were time walking (n = 31, 63%), sitting (n = 28, 57%), standing (n = 28, 57%), lying (n = 27, 55%), and upright – combining standing and walking – (n = 26, 53%). Other outcomes were time spent being sedentary (n = 13, 27%; combining lying and sitting), cycling (n = 3, 6%), running (n = 1, 2%), a category combining sitting and standing (n = 1, 2%), a category combing all postures and motion besides lying (n = 1, 2%), and walking stairs (n = 1, 2%). The wearing positions of the devices were the thigh (n = 27, 55%), thigh + lower leg (n = 7, 14%), thigh + chest (n = 6, 12%), chest (n = 4, 8%), lower back (n = 3, 6%), thigh + upper arm (n = 1, 2%) and thigh + chest + ankle (n = 1, 2%). The complete device characteristics can be found in Table 2 and Figures 2(b–d).

Table 2. Device, measurement & analysis specifications.

 	Device Characteristics	 	 	 	 	Measurement characteristics	 	Analysis	 	
Study, year	Name device (developer)	Type of sensor	Sensor units (n)	Sensor locations	Sensor outcome	Device vali-dation scoreB	Attachment method	Wear time (hour /day)	Max/mean meas. duration [days] (T1)	Sample frequency raw data/ processed data/ epoch length	Valid day	Analysis period	Analysis Software	
Askim et al. (2013) [58]	Pal2 with tilt switch (PAL Tech./ Gorman ProMed)	Uniaxial AC with tilt switch	1	Thigh (AP), lower leg (tilt switch)	L/Si/Up	1	Elastic strap with velcro	24	1/n.r.	10 Hz/n.r.	<30min non-wear	Full 24h	Pal2calcs package	
Baldwin et al. (2020) [57]	ActivPAL 3 (PAL Tech.)	AC	1	Thigh	Sd/Up	2	n.r.	24	1/at least 24h	n.r./ n.r./ 60s	<6h non-wear	Full 24h	ActivPAL v.7.1.18A	
Bendix et al. (2023) [31]	SENS motion PLUS v1.7.6 (n.r.)	Triaxial AC	2	Thigh, chest	L/Si/St/W	0	Adhesive tape	24	7/med 6.96	12 Hz/ n.r./ 5s	n.r.	n.r.	n.r.	
Borges & Carvalho (2012) [56]	Dynaport Move- Monitor (McRoberts)	Triaxial AC	1	Lower back	L/Si/St/W	1	Elastic band	12	2/n.r.	n.r.	n.r.	8am-8pm	Analysed by manufacturer	
Borges et al. (2015) [55]	Dynaport Minimod (McRoberts)	Triaxial AC	1	Lower back	L/Si/St/W	1	Elastic band	12	2/n.r.	n.r.	n.r.	8am-8pm	Analysed by manufacturer	
Conijn et al. (2020) [54]	Activ8 Prof. Activity Monitor (2M Engineering)	Triaxial AC	1	Thigh	L/Si/St/ W/C/R	1	Adhesive tape	24	6 or discharge/ n.r.	n.r.	n.r.	730am-22pm	In device	
Dall et al. (2019) [53]	n.r.	Triaxial AC	2	Thigh, chest	L/Si/St/W	0	Adhesive tape	24	7 or discharge/ 3.5	12.5 Hz/ n.r./ 10s	n.r.	n.r.	n.r.	
Davenport et al. (2015) [52]	ActivPAL (PAL Tech.)	Uniaxial AC	1	Thigh	L/Si/Up/W	1	n.r.	24	7 or discharge/ n.r.	n.r.	24h	Full 24h	n.r.	
De Oliveira et al. (2021) [63]	ActivPAL 3 (PAL Tech.)	AC	1	Thigh	Sd/St/W	0	Adhesive tape	24	5 or discharge/ n.r.	n.r	10h	n.r.	ActivPAL	
Evensen et al. (2017) [51]	ActivPAL (PAL Tech.)	AC	1	Thigh	Up	2	Adhesive tape	24	Discharge/ 5.39	n.r.	24h	Full 24h	ActivPAL v.7.3.32	
Floegel et al. (2018) [50]	ActivPAL 3c (PAL Tech.)	Micro-AC	2	Thigh, chest	L/Si/St/W	1	Adhesive tapeA	24	Day after discharge/ 5.1	n.r./ n.r./ 15sA	n.r.	Full 24h	n.r.	
Floegel et al. (2022) [30]	ActivPAL 3c (PAL Tech.)/Tractivity (Kineteks Corp)	Micro-AC / AC	2 / 1	Thigh, chest and ankle	L/Si/St/W	1	Adhesive tape	24	Discharge/ 5.1	n.r./ n.r./ 15s	n.r.	n.r.	n.r.	
Fuchita et al. (2023) [24]	ActivPAL (PAL Tech.)	AC	1	Thigh	L/Si/St/W	2	Adhesive tape	24	7 or discharge/ med 4	10 Hz/ n.r./ 15s	24h	Full 24h	PALanalysis v.8.11.8.75	
Gilmore et al. (2020) [49]	ActivPAL 3 (PAL Tech.)	AC	1	Thigh	Sd/St/W	2	Adhesive tape	24	7 (in/out of hospital)/ n.r.	n.r.	n.r.	Full 24h	ActivPAL	
Halfwerk et al. (2021) [62]	AX3 (Axivity)	AC	2	Thigh, upper arm	L/Si/St/W/C/ walking stairs	2	Adhesive tape	24	7 or discharge/ med 4	n.r./ n.r./ 2.56s	n.r.	7am-11pm	Own algorithm	
Hartley et al. (2018) [47]	AX3 (Axivity)	AC	2	Thigh, lower leg	L/Si/St/Up	2	Adhesive tape	24	2/n.r.	100 Hz/ n.r./ 5s	n.r.	Full 24h	AX3 OMGUI software v.38	
Hartley et al. (2020) [48]	AX3 (Axivity)	AC	2	Thigh, lower leg	L/Si/St/W	2	Adhesive tape	24	7 or discharge/ n.r.	100 Hz/ n.r./ 5s	n.r.	First 24h	AX3 OMGUI software v.38A	
Harvey et al. (2018) [46]	ActivPAL (PAL Tech.)	Uniaxial AC	1	Thigh	Sd/St/W/Up	0	Adhesive tape	24	2/n.r.	n.r.	n.r.	Full 24h	ActivPAL	
Haslam-Larmer et al. (2021) [61]	ActivPAL (PAL Tech.)	Triaxial AC	1	Thigh	L/Si/W/Up	1	Adhesive tape	24	10 or discharge/ 3.78	n.r.	24h	Full 24h	ActivPAL	
Howie-Esquivel & Zaharias (2013) [45]	Micro Care Timeliness Monitors (Augment Tech)	Triaxial AC	2	Thigh, lower leg	L/Si/Up	2	Adhesive tape	24	5 or discharge/ n.r.	1 Hz/ n.r.	n.r.	n.r.	Hyper Terminal PE program	
Jawad et al. (2022) [29]	ActivPAL 3 (PAL Tech.)	Triaxial AC	1	Thigh	Sd/St/W/Up	1	Adhesive tape	24	7/4	20 Hz/ n.r./ 15s	20h	Days 1-6	ActivPAL v.7.2.32	
Jeldi et al. (2016) [44]	ActivPAL 3 (PAL Tech.)	AC	1	Thigh	Up	1	Adhesive tape	24	Discharge/ n.r.	n.r.	n.r.	n.r.	ActivPAL v.7.1.18	
Jones et al. (2024) [20]	ActivPAL 4 (PAL Tech.)	AC	1	Thigh	Sd/St/W	0	Adhesive tape	24	n.r./Med 11	n.r./ n.r./ 15s	10h	Full 24h	In device	
Karlsen et al. (2017) [43]	ActivPAL (PAL Tech.)	Triaxial AC	1	Thigh	Sd/Up	0	n.r.	24	Discharge/ n.r.	n.r.	n.r.	n.r.	n.r.	
Katz et al. (2022) [28]	Dynaport Move Monitor+ (McRoberts)	Triaxial AC, Magn, Gyro	1	Lower back	Si/St/W/Up	1	Elastic band	24	14 or discharge/ n.r.	100 Hz/ n.r.	n.r.	2-4 days	Move Monitor v.3.0	
Kawamura et al. (2019) [42]	WHS-1 (Union Tool Co.)	Triaxial AC	1	Chest	Up/W	0	ECG electrodes	24	7/med 6	31.25 Hz/ n.r.	<6h non-wear	Full 24h	n.r.	
Kerr et al. (2016) [41]	ActivPAL (PAL Tech.)	Triaxial AC	1	Thigh	L/Si/St/W	0	Waterproof materials	24	2/n.r.	20 Hz/ n.r.	n.r.	Full 24h	ActivPAL	
Kersten et al. (2023) [17]	Orthronic Smart Knee (n.r.)	AC, Magn, Gyro	2	Thigh, Lower leg	L/Si/St	0	n.r.	24	Discharge/ 6	25 Hz/ 1 Hz/ n.r.	n.r.	6am-8pm	n.r.	
Kirk et al. (2022) [27]	ActivPAL 3 (PAL Tech.)	Triaxial AC	1	Thigh	Up	1	Adhesive tape	24	Discharge/ med 5	n.r.	1 full day	n.r.	ActivPAL v.7.2.38	
Koenders et al. (2019) [40]	Healthpatch (Vital Connect)	Triaxial AC	1	Chest	L/(S + St)/W	1	n.r.	24	4 or discharge/ med 3	1 Hz/ n.r.	n.r.	7am-1159pm	n.r.	
Kramer et al. (2013) [39]	ActivPAL 2 with tilt switch (PAL Tech.)	Dual-axis AC with tilt switch	1	Thigh (AP), lower leg (tilt switch)	L/Si/Up	1	Elastic straps with velcro	9	1/n.r.	10 Hz/ n.r.	n.r.	8am-5pm	PAL2calcs v. February 2010	
Kronborg et al. (2016) [38]	ActivPAL 3 (PAL Tech.)	AC	1	Thigh	Up	2	Adhesive tape	24	10/med 6	n.r.	n.r.	Full 24h	ActivPAL3 v.7.2.29	
Lehmkuhl et al. (2023) [23]	AX3 (Axivity)	AC	2	Thigh, Chest	L/Si/St/W/C	2	Adhesive tape	24	ICU Discharge/ 7.6	25 Hz/ n.r.	>12h	Full 24h	OmGUIv.1.2.2.37	
Mavroeidi et al. (2019) [16]	ActivPAL 3 (PAL Tech.)	Triaxial AC	1	Thigh	Si, Up	1	Adhesive tape	24	4/n.r.	n.r.	n.r.	6am-11 pm	ActivPAL v.7.2.32	
Nakanishi & Goto (2023) [18]	MyBeat (UNION- TOOL Corp.)	AC	1	Chest	L/Si/St/W	0	n.r.	9am- 5pm	Day before discharge/ 3.5	n.r.	n.r.	9am-5pm	Python v.3.9.16	
Norvang et al. (2018) [37]	ActivPAL (PAL Tech.)	Triaxial AC	2	Thigh, chest	L/Si/Up	2	n.r.	24	14 or discharge/ 5.8	10 Hz/ n.r.	n.r.	n.r.	n.r.	
Oestergaard et al. (2018) [19]	ActivPAL (PAL Tech.)	AC	1	Thigh	Sd/Up	0	n.r.	24	Hospitalization/n.r.	n.r.	n.r.	Full 24h	n.r.	
Pedersen et al. (2013) [36]	Wireless monitors (Augmenta-tive Inc.)	AC	2	Thigh, lower leg	L/Si/Up	2	n.r.	24	10 or discharge/ 4.4	n.r.	<6h non-wear	Full 24h	n.r.	
Piotrowicz et al. (2023) [22]	ActivPAL 3 (PAL Tech.)	AC	1	Thigh	Si/St/W	0	n.r.	24	2 times 24h	n.r.	n.r.	n.r.	Manufacturer Software	
Porserud et al. (2019) [35]	ActivPAL 3 micro (PAL Tech.)	AC	2	Thigh, chest	L/Si/St/W	1	Adhesive tape	24	5 or discharge/ min 3 days	n.r.	<12h non-wear	First 3 post-operative days	n.r.	
Ramsey et al. (2021) [60]	ActivPAL 4 (PAL Tech.)	Triaxial AC	1	Thigh	L/Si/Sd/ St/W/Up	1	n.r.	24	7 days (in/out of hospital)/ med 6	20 Hz/ n.r./ 15s	<4h non-wear	Full 24h	n.r.	
Rojer et al. (2021) [59]	ActivPAL 4 (PAL Tech.)	Triaxial AC	1	Thigh	L/Si/Sd/Up	0	n.r.	24	7 or discharge/ med 6	20 Hz/ n.r.	<4h non-wear	Full 24h	ActivPAL generation 8	
Sheedy et al. (2020) [34]	ActivPAL (PAL Tech.)	AC	1	Thigh	Sd/St/W	1	Adhesive tape	24	14 or discharge/ min 3 days	n.r.	n.r.	Full 24h	ActivPAL	
Theou et al. (2019) [33]	ActivPAL 3 VT (PAL Tech.)	AC	1	Thigh	Up	1	n.r.	24	14 or discharge/ min 1 day	n.r./ n.r./ 15s	n.r.	Full 24h	n.r.	
Turan et al. (2023) [25]	ViSi monitor (n.r.)	AC	1	Chest	Up/L/W	2	n.r.	24	n.r.	n.r./ n.r./ 15s	12h in 2 days	Full 24h	n.r.	
Van Dijk-Huisman et al. (2020) [9]	MOX (Maastricht Instruments B.V.)	Triaxial AC	1	Thigh	Sd/Up	1	Adhesive tape	24	Discharge/ n.r.	25 Hz/ n.r./ 1s	<4h non-wear	n.r.	In device	
Van Dijk-Huisman et al. (2023) [21]	MOX (Maastricht Instruments B.V.)	AC	1	Thigh	Sd/St/W	2	Adhesive tape	24	7 or discharge/ n.r.	25 Hz/ n.r./ n.r.	20h	Full 24h	In device	
Villumsen et al. (2015) [32]	ActivPAL (PAL Tech.)	Triaxial AC	1	Thigh	Up/St/W	1	Adhesive tape	24	Discharge/ n.r.	20 Hz/ n.r.	24h	Full 24h	ActivPAL v.6.3.0	
Woo et al. (2021) [26]	ActivPAL 3 (PAL Tech.)	Triaxial AC	1	Thigh	St/W	1	Adhesive tape	24	7/n.r.	n.r.	n.r.	Days 2-6	n.r.	
AC: accelerometer; Magn: magnetometer; L: Lying, Gyro: gyroscope; Si: Sitting, Sd: Sedentary, St: standing, Tech.: Technologies, W: walking, Up: Upright; n.r.: not reported; med: median A reported in reference; B Validation score: 0 no reference/validation; 1 validated in non-clinical population; 2 validated in clinical population.

Measurement characteristics

Most studies instructed to wear the devices 24 hours a day (n = 45, 92%). The intended measurement duration was described in the methodology of studies as a specific number of days (n = 17, 35%), until discharge (n = 9, 18%), until the day before or after discharge (n = 2, 4%), for a specific time (n = 1, 2%) or was not reported (n = 2, 4%). The remaining studies (n = 18, 37%) intended to measure until discharge or for a maximum number of days – varying from 4 to 14 days – depending on which event occurred first. The actual measurement duration was not reported in the majority of studies (n = 23, 47%), or was described with a minimum (n = 5, 12%), mean (n = 11, 22%), or median (n = 10, 20%) number of days.

Analysis characteristics

Not all studies were clear about device settings and data processing. Twelve (24%) reported the raw outcomes of their device, and 21 (43%) of studies reported the sampling frequency of the raw data. One study (2%) reported the sampling frequency of processed data, which is the frequency of the outcomes used in the study. Only thirteen (27%) of studies reported the used epoch length. Finally, if data processing occurred using an online tool and what software versions were used was often unclear.

Most studies did not cover how removal of the device during a measurement period was handled in terms of the results (n = 42, 86%). The remaining studies dealt with device removal in different ways: not allowed (n = 1, 2%), allowed (n = 2, 4%), or both allowed and registered in a diary (n = 4, 8%). How a valid measurement day was defined was not reported in most studies (n = 29, 59%). The remaining studies set the threshold for valid days on 24 h (n = 6, 12%), 23.5 h (n = 1, 2%), 20 h (n = 5, 10%), 18 h (n = 3,6%), 12 h (n = 3, 6%), or 10 h (n = 2, 4%). The entire measurement period was analysed in 21 (42%) studies. Six studies (12%) analysed specific days, and nine (18%) analysed only a specific time of the day, for example, during the daytime. The remaining 13 studies (27%) did not report which period was analysed.

Time spent sleeping was handled differently; most studies (n = 22, 45%) included time spent sleeping in the analysis, 7 (14%) excluded sleeping from the analysis, and the remaining studies (n = 20, 41%) did not specify how they handled sleep time.

Discussion

This review describes both research applications and methodological approaches of objective sensor technology to measure postures and motions in the hospital setting. It includes 49 papers with various device applications, study goals, and patient populations. Fifteen different brands of sensors were studied. The measurement characteristics (such as measurement duration) and analysis characteristics (such as analysis software and settings) studied varied considerably between studies. Assessing the studies was challenging due to frequent insufficient reporting of measurement methods and data analysis characteristics, thereby limiting the possibilities for reproduction, interpretation, and generalization.

This review focused on hospitalized patients as their physical behaviour is different than non-clinical rehabilitation populations. An important difference relates to the way hospitalized patients move. This is characterized by slow walking speed, using walking aids, and low mobility, especially in older adults [32,64,65]. These differences mean that consumer-grade devices may be less suitable in hospitalized populations. Physical behaviour might change considerably within a few days due to physical recovery or lower pain and fatigue levels. Clinical measurements must also deal with the setting specific consequences, like the influence of external factors (e.g. scheduled examinations and visiting hours). For adequate data interpretation and its use in treatment, systematically recording these patient-related and external factors is necessary. Some studies used a diary, questionnaires, or retrieving information from medical records. Physical behaviour research in hospital rehabilitation is characterized by varying and often unpredictable measurement periods due to differences in the patient population (between group variation) and unforeseen discharge moments (within-group variation). For example, we noticed both shorter admissions (e.g. after upper limb injury) and longer and more complex admissions (e.g. ICU patients). The specific population will affect the measurement period, this needs to be considered in study designs.

Body posture and motion measurements were done using a variety of research applications and in different patient populations. The application category describing and exploring (changes in) postures and motions was the largest (n =19,39%), followed by the category that aimed at investigating the relationship between postures and motions and other parameters such as muscle strength or length of stay (n =18,37%). On the other hand, the development of new measurement methods (n =5,10%) or application in intervention studies was relatively rare (n =7,14%), and these study aims were not performed more frequently in the most recent years.

Physical behaviour has several components and we focused on postures and motions, which affect the choice of devices and the configuration of sensor units. Monitoring physical activity intensity is often performed using sensors on the waist or wrist. The wrist or waist are not very accurate for monitoring postures and motions compared to the thigh [66,67]. Recent signal analytical techniques did show some potential for posture and motion classification using wrist of waist sensors [68–71]. The thigh remains the most logical and optimal position, as shown by the studies in our review. Even with sensing at the thigh distinguishing between sedentary postures, such as lying and sitting, can be challenging [72]. Therefore, some studies [17,23,30,31,36,37,39,45,47,48,50,53,58,62,73] used multiple sensors to distinguish between relevant postures and motions. Using multiple sensors is a trade-off as it makes measurements more complex and less feasible, which is undesirable and should be avoided if possible [74,75].

Altered movement patterns in hospitalized patients have consequences for the validity of the devices used. We found that only fourteen (29%) included studies used devices that were validated in a hospitalised population. These referred validation studies did not validate all postures and motions, e.g. upright time instead of standing and walking separately [76], because detecting walking or steps is difficult with lower walking speeds [77–79]. As an alternative, some included studies only measure upright activity, as they may be unable to validly differentiate between walking and standing [9,16,19,29,33,36–39,43–45,47,51,57–59]. It is important to use devices that have been validated in hospital populations [80].

Our review included at least 20 different devices from 10 developers, with even larger variability in the data processing and analysis performed, e.g. using various definitions of valid days and analysing daytime or full 24-hour data. A partial solution for this variability in analysis, is the collection of raw data, to unify outcomes using identical data processing methods [11,12,81,82]. If raw data are collected then, despite some small differences between devices, similar open-access algorithms can reprocess data to facilitate the comparison and reproduction of study results and to overcome collection problems with different devices and consider the FAIR data principles.

Most studies failed to report adequately on the methods of measurement and analysis. Little information was available on data collection and analysis, while this influences the results [83]. Detailed reporting based on guidelines therefore is crucial, as also recommended by Montoye et al. (2018) and Ward et al. (2005) [81,84].

However, even in cases of detailed reporting, heterogeneity in the methodology of measurements and analysis is a serious issue, complication the interpretation and comparison of study results. This heterogeneity often stems from methodological choices, influencing outcomes. For instance, while some studies focus solely on specific periods like waking hours (8am to 5 pm) within a 24-hour data collection window [17,18,39], others analyse the entire measurement period [20,21,24,26,28–36,38,41,42,46–52,57–61,85]. Heterogeneity not only affects the validity of analyses but also hinders study comparisons. Thus, it falls upon the researcher to carefully select the appropriate method for their data collection and analysis in the specific setting or population [75]. However, striving for harmonization in data collection practices will benefit the advancements in the field.

Previous studies have attempted to address this challenge of heterogeneity by providing accelerometer guidelines aimed at improving the standardization across various methodological aspects in physical activity interventions [12,81,82,84,86,87]. Despite these efforts, differences persist in existing guideline recommendations, reflecting the different needs of research populations and outcomes of interest, such as counts, exercise intensity, or postures and motions. Given the variety in methodology observed in our review, there is a clear need for additional recommendations tailored to hospital patients. Therefore, we recommend a future collaborative effort to develop consensus guidelines tailored to the specific needs of these measurements in a hospital population. In light of the variety in methodology we found in this review, establishing consensus guidelines would benefit the standardization of research practices.

Strengths and limitations

A strength of our review was the comprehensive and systematic literature search performed by two reviewers independently from each other. Another strength of the data extraction process was using a personal standardized format to extract relevant information from the included papers thoroughly. However, data extraction was performed by a single reviewer, which increases error possibilities and is a limitation of this study. A final limitation is the exclusion of studies published in other languages than English.

Conclusions

In conclusion, monitoring postures and motion with accelerometers was performed with a wide variety of device applications, study goals, and in various hospitalised populations. The studies need more precise and complete reporting and there was heterogeneous data collection and analysis methodology. The heterogeneity limits the possibilities for reproduction, interpretation, and generalization of the study results. Measuring postures and motion in hospitalised populations differs from other populations. Studies should adjust their device choice, measurements, analyses, and reporting to suit this population. Clear guidelines for reporting and the collection and sharing of raw data would benefit the field by enabling study comparison and reproduction.

Supplementary Material

Supplementary_Material_A1.docx

Acknowledgements

The authors wish to thank Sabrina Gunput and Maarten Engel from the Erasmus MC Medical Library for their help in developing and executing the search strategies for this review.

Author contributions

M.L.B., H.L.H., J.B.J.B. and J.A.N.V. were involved in conception and design, revising of the article and final approval of the version to be published. M.L.B., H.L.H. and J.B.J.B. in analysis and interpretation of the data and the drafting of the paper. All authors agree to be accountable for all aspects of the work.

Disclosure statement

The authors declare that they have no conflicts of interest with respect to the research, authorship, and/or publication of this manuscript. No financial or personal relationships with individuals or organizations have influenced or could be perceived to have influenced the work presented in this paper.

Data availability statement

The data that support the findings of this study are available from the corresponding author, M.L.B., upon reasonable request.
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