Quantified Health: A Feasibility Study on a Sensor-Based Feedback

and Assistance System in Cardiology, Oncology and Orthopaedics

Anne Grohnert

, Michael John

, Benny Häusler

, Christian Giertz

, Mirko Wolschke

, Jana Liebach

Rona Reibis

, Anne Klemmer

, Lisa Konrad

, Silke Kollath

and Jan C. Zoellick

Fraunhofer FOKUS, Kaiserin-Augusta-Allee 31, 10589 Berlin, Germany

Orthopaedics and Oncology clinic, Reha-Zentrum Lübben, Postbautenstraße 50, 15907 Lübben, Germany

Kardiologische Gemeinschaftspraxis am Park Sanssouci Potsdam, Zimmerstrasse 7A, 14471 Potsdam, Germany

Theraphysia GmbH, Hellersdorfer Straße 77, 12619 Berlin, Germany

Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin,

Charitéplatz 1, 10117 Berlin, Germany

Keywords: Mobile Assistance Systems, Sensor-Based Feedback, Telemedicine, Digital Care, Everyday Care.

Abstract: This paper reports the results of the Quantified Health project that developed a complex, digitally supported

intervention. The project provides insights into how a sensor-based system can be organizationally integrated

into the existing workflows of everyday treatment. The concluding pilot study took place in three medical

facilities and addressed patients of orthopaedic, oncologic and cardiologic diseases in an in- or outpatient

therapeutic setting. As a study result from the user's perspective, it is very appreciated to objectify the patient’s

health related behavior. Care providers considered it positive that they received more data from patients'

everyday lives and that the improved data situation can lead to more sustainable care. On the other side, the

time required to integrate a new digital application into the tightly scheduled daily treatment routine was

perceived as a hindering factor. Nevertheless, the results of the study show that a more generic sensor-based

assistance system could be used for different diseases and cross sectoral. Furthermore, the constant contact

with therapists increases patients' motivation to engage in health-preserving activities (self-regulation).

1 INTRODUCTION

In order to maintain or optimize the success of

temporary rehabilitation measures, longer-term

sustainable therapy is required. Particularly after an

inpatient stay, patients necessarily do not economize

movement sequences and control exercise limits

under everyday conditions (Thimmel et al., 2018).

During rehabilitation, the patient is subject to

constant supervision by doctors and therapists. In

contrast, after rehabilitation, the patient may

increasingly fall back into habitual behavioural

patterns. In the case of heart and pulmonary diseases,

but also in the field of oncology and orthopaedics,

most patients often have fears and inhibitions when

they return to work or everyday life. This leads to the

patient avoiding regular physical activities such as

walking or cycling. However, excessive intensity of

exercises can also have serious consequences for the

patient: Patients who were previously very active in

sports often exert themselves too much despite their

limited physical functionality.

Individualized therapy can help to strengthen the

general health condition and body awareness and to

consolidate the therapeutic progress already achieved

during rehabilitation. The possibility of objectifying

the own behavior with support by sensory feedback

and assistance systems offers a great opportunity for

patients to permanently change their own behavior

(Plaete et al., 2015). This helps patients to manage

better with everyday life and to exert themselves

more economically in their regular routines. By

improving their mobility in daily routines, patients

experience an increase in their quality of life, which

motivates them to live healthier and more sustainable

(self-motivation and self-control).

Especially in times like Covid 19, telemedical

feedback and assistance systems help to reduce

physical contact between patients and doctors while

maintaining therapeutic measures and treatment

quality (Omboni et al., 2022). Patients are supported

in their daily routines and healthcare professionals

retain control of the treatment process through

628

Grohnert, A., John, M., Häusler, B., Giertz, C., Wolschke, M., Liebach, J., Reibis, R., Klemmer, A., Konrad, L., Kollath, S. and Zoellick, J.

Quantiﬁed Health: A Feasibility Study on a Sensor-Based Feedback and Assistance System in Cardiology, Oncology and Orthopaedics.

DOI: 10.5220/0012431100003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 628-637

ISBN: 978-989-758-688-0; ISSN: 2184-4305

integrated feedback functionalities without the need

for physical presence.

Thus, the "Quantified Health" project focused on

investigating the factors and structures of telemedical

assistance systems that are both beneficial and

detrimental to the acceptance, use and satisfaction of

users, i.e. care providers and patients. The system was

testet with a variety of indications from different

medical disciplines in order to figure out how a

telemedical assistance system can contribute to cross-

indication, cross-specialty and cross-sector treatment

processes.

To assess the feasibility and usability of the

Quantified Health system an accompanying pilot

study was carried out at the rehabilitation-center in

Lübben (oncology, orthopaedics), the cardiology

practice at Sanssouci Park and at the orthopaedic

practice Theraphysia. For this purpose, 56 patients

were recruited during their stay in the participating

clinics or by the outpatient facilities and were

supervised by medical staff who were consulted about

this care approach in form of interviews.

2 RECENT WORK

Due to our knowledge, the approach of using a

telemedical assistance system across all indications

and sectors has not yet been adequately considered.

Research has already been conducted on sensor-based

feedback and assistance systems for individual

indications. Naeemabadi et al., 2020 provide an

overview of studies of sensor-based feedback systems

that were used for specific indications in the field of

orthopaedics (e.g. knee and hip endoprosthetics).

Eichler et al., 2019 investigated the use of a system

for movement therapy with real-time feedback based

on optical sensor technology in an evidence study, but

also for specific indications in the field of

orthopaedics. For cardiological diseases, Kumazawa

et al., 2022 examined the use of a training system with

computer-generated animations and Zhong et al.,

2023 provide an overview of the long-term effects of

cardiac remote rehabilitation for patients with

coronary heart disease. In a systematic review, Brick

et al., 2023 state that further research with more

diversified samples, common measures of disability

and pragmatic study designs are needed in the field of

oncology to advance telemedicine in cancer

rehabilitation due to the heterogeneity of the diseases.

Algarni et al., 2022 found in a survey of patient

perceptions of tele-rehabilitation across indications

that most patients are very or moderately confident

that therapists can successfully assess and treat their

problems using tele-rehabilitation. Wang et al, 2023

also used a survey to investigate the intention to use

and factors influencing the use of telerehabilitation to

treat patients with cross-indication rehabilitation

needs. Both studies did not consider the provider's

perspective.

In addition to the studies mentioned above, the

“Quantified Health” project investigated whether and

how a telemedicine system with specific functions

can be used across indications and sectors. The

evaluation of the technical feasibility and the

acceptance of the system from the perspective of

providers and patients were central to this research.

3 QUANTIFIED HEALTH

SYSTEM

The Quantified Health system consists of several

components (see Figure 1). On the one hand, there is

the mobile patient app, which can use different

commercially available wearables as a data source

and, on the other, the therapy application for medical

staff to view the patient's individual data. The

Quantified Health Server serves as a central data

collection point and makes the data available to the

applications via secure transport.

Figure 1: System components of the Quantified Health

system.

3.1 Quantified Health App

The mobile component for patients consists of an app

and body-closed sensors in form of a smartwatch.

There are also input options for weight, blood

pressure and general well-being. The current vital and

activity parameters are displayed on the smartphone

app in the form of color-coded value fields. Similar to

a traffic light system, the colors of the fields provide

real-time feedback on the preset range of the vital

parameters, which are determined by the physician or

therapist. For example, the value field for heart rate

turns red if the heart rate rises above an individually

defined limit.

Quantiﬁed Health: A Feasibility Study on a Sensor-Based Feedback and Assistance System in Cardiology, Oncology and Orthopaedics

629

The training plan that the patient has agreed with

their doctor provides an overview of the goals that

have been set. The patient can switch between

everyday and training mode. There are 4 different

training categories available in the training mode:

Running, cycling, ergometer and an individual

training plan with various gymnastics exercises.

Training explanations also show statistics and

progress in relation to the current training plan.

Various vital and movement parameters are also

recorded in training mode, such as the number of

steps, distance covered and heart rate. The users can

describe their current condition in the diary. There is

also the option of rating the current mood on a scale

of 1 to 10 in the event of sudden indisposition. If

required, the current location (e.g., at home or at

work), the feeling of stress and specific symptoms are

also queried.

For simple and direct communication, e.g., if

doctors and therapists have noticed a health risk or

persistent stress, the app provides a bilateral

communication option in form of video conferencing.

3.2 Therapy-Application

The therapy application for doctors and therapists is a

tool with a graphical interface that medicals can use

to create and edit health data records for patients

(Patient Management System - PMS). These data

records contain information about patients (socio-

demographic data, medical history) and training plans

(e.g., recommendations for the number of steps). The

healthcare professional can set individual load limits

for everyday and training modes (e.g., individual

heart rate limit). In addition, training results and

relevant vital and activity data are graphically

processed and aggregated in the therapy application

so that the attending physician/therapist can identify

any trends in the patient's state of health. For example,

the average stride length and speed when running and

cycling are determined from the movement data of

the training mode. The training results and therapy-

relevant data stored in the system can also be used to

adapt individual therapy plans as part of a suggestion

system.

3.3 Quantified Health-Backend

The Quantified Health-Server stores all data of the

telemedical therapy and training system, i.e.,

participating patients, therapists, therapy plans and

training results. It is also responsible for the

synchronization and persistent storage of data from

all subsystems involved (mobile systems, therapy

application). The data collected and stored in the

project context are master data, health data and

identification data. These data are subject to special

protection requirements and are safeguarded by state-

of-the-art technical and organizational measures in

accordance with the GDPR.

4 MATERIALS & METHODS

The aim of the "Quantified Health" project was to

pilot and evaluate a complex, digitally supported

intervention from a user perspective in three medical

facilities across different diseases and sectors of the

german health care system. The use of a mobile

sensor-based assistance system was tested, which

accompanies patients with cardiological, orthopaedic

and oncological diseases in their everyday life as part

of follow-up treatment or aftercare.

Physiological deficits were determined and based

on sensory data, everyday stress situations were

analysed in real-time under medically and

therapeutically defined aspects and the patients

obtained immediate feedback on their state of health.

The medical staff was involved by receiving vital and

movement data from patients and by providing

feedback (e.g., manual modifications of activity

recommendations by the doctor and therapist

providing care) to the patients. This should help the

patient to manage better their health condition and

experience greater economy under physiological

load.

The 56 patients were recruited in the 3 facilities in

different ways (e.g. by direct communication, by a

displayed presentation explaining the study objective

and procedure installed on the practice TV screen in

waiting room), but based on similar inclusion and

exclusion criteria:

• Resilience according to the health status (e.g.

oncology: no metastases, orthopaedics: no

acute ischialgia, cardiology: no unstable

cardiac arrhythmia)

• Compliance required for physical activity (e.g.

extracardiac comorbidities or orthopaedic

limitations)

• Necessary affinity for technology (suitable

mobile device, available e-mail address,

digital questionnaires)

In all three facilities, separate appointments were

made with the patients for patient information and

education, data protection information, declaration of

consent, technical training and the creation of patient

data.

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630

For the data collection, patients were provided

with a smartwatch with sensors for measuring vital

and movement parameters for a period of 3-5 months.

As the data is particularly sensitive data under the

GDPR, appropriate technical and organizational

measures were implemented to protect the data. The

basis for data processing on the side of the patients

was based on a consent. In accordance with the

principle of data minimization, data was only

collected for the purpose of treatment within the study

and evaluated pseudonymously. Data transmission

was encrypted in accordance with current security

standards. All ethical and regulatory requirements

were safeguarded by obtaining an ethics vote. The

study was also published in the German Clinical

Trials Register.

The vital and movement parameters were

evaluated at a total of four measurement points (upon

enrolment (implementation and baseline), on

transition from supervision by the medical institution

to sole responsibility (approximately 4 weeks after

enrolment), 13 weeks after enrolment (end of the

intervention period), optional follow-up 21 weeks

after enrolment) and the subjective evaluation of the

tool was recorded in questionnaires.

The analyses included data from training sessions

that took at least 600 seconds (Bull et al., 2020) and

were within the time frame of enrolment (t0) to 13

weeks after enrolment (t2). Training sessions between

t2 and t3, as well as training sessions lasting 300-600

seconds, were considered separately. Trainings after

t3 and with a duration of less than 300 seconds were

not considered.

In addition to standardized socio-demographic

questions, validated scales were used as

questionnaires (Van der Laan et al., 1997),

(Venkatesh et al., 2003), (Davis et al., 1989), (Borg,

1998), (Bullinger, 1995), (Neyer et al., 2012),

(Stoyanov et al., 2016), (Parmanto et al., 2016). In

parallel, guided, semi-structured interviews were

conducted with two practitioners from each of the

three different medical facilities to obtain an

assessment of the system's everyday viability on the

side of the care providers.

The evaluation concept therefore used elements

from process-oriented formative evaluation research

(qualitative interviews) and effectiveness-oriented

summative evaluation research (quantitative app and

questionnaire data), which together assessed the

progress and feasibility of the Quantified Health

application.

5 RESULTS

The results presented below are based on data

recorded by the system. This necessarily represents a

reduced section of reality. It does not include, for

example, training units and steps that were completed

without using the system or training units that were

carried out with the system but were insufficiently

synchronized and transmitted.

A total of 56 people took part in the feasibility

study. They were on average 46 years old (SD = 16.58

years); 55% were female. Table 1 shows the socio-

demographic characteristics of the participants at the

three facilities.

Table 1: Socio-demographic characteristics of the study

sample by institution.

Total Onco. Cardio. Ortho.

N 56 21 18 17

Age (M, SD)

46.45

(16.58)

38.29

(10.39)

58.56

(14.22)

43.35

(18.23)

Gender (fem.)

55%

76%

33% 53%

BMI (M, SD)

26.40

(4.64)

26.05

(4.44)

26.03

(4.19)

27.28

(5.49)

5.1 Training Frequency

A total of 1053 training sessions with a duration of

≥600 seconds were recorded for the 56 participants

over a period of 13 weeks. This corresponds to an

average training frequency of M = 1.45 training

sessions (SD = 2.33) per week. There were two

outliers with 8.23 and 13.38 training sessions per

week. No training was documented for eight

participants. Excluding these ten patients resulted in

an average weekly documented number of training

sessions of M = 1.29 (SD = 1.43). The average

training frequency of all participants varied

depending on the time point and showed a decreasing

trend with increasing duration of participation. The

training frequency in the first weeks of participation

was thus significantly higher than the training

frequency at the end of the intervention period. Figure

2 shows the progression of the training sessions over

time.

5.2 Feasibility - Dropout

For eight of the 56 participants, no training was

documented (14%). Beyond that, for 17 people

(30%), no training was recorded from week 5 no

onwards; after two thirds of the time (8 weeks),

Quantiﬁed Health: A Feasibility Study on a Sensor-Based Feedback and Assistance System in Cardiology, Oncology and Orthopaedics

631

Figure 2: Number of documented weekly training sessions

over the 13-week intervention period. The blue bars show

the unadjusted data of all participants (n=56) incl. dropouts

and outliers. The gray bars show adjusted data of

participants who trained continuously (n=25) excluding

dropouts and outliers. The training target of two training

sessions per week was mainly achieved in the first weeks of

participation.

training was recorded for a further four people (7%).

This left 27 people (48%) whose training sessions

were recorded by the system after 8 weeks. Looking

only at those participants for whom training sessions

were recorded up to the end of the 13-week period (n

= 25), the training frequency was at the targeted level

(M = 2.01; SD = 1.59).; meaning just under half of

the participants completed training sessions over the

longest period of the project. Dropout rates of up to

30-50% are not uncommon in feasibility studies

(Fjeldsoe et al., 2010 & Pfaudler et al., 2015). Figure

3 shows the remaining patients since the beginning,

after 4 weeks and after 8 weeks, broken down by

indications.

Figure 3: Number of people for whom training sessions

were documented up to 4 weeks, for whom training sessions

were documented after 4 weeks of participation and for

whom training sessions were still documented after 8 weeks

of participation.

5.3 Therapy Success

Health-related well-being was measured using the

SF-36 with the subscales physical health and mental

health (Bullinger, 1995). A total of 49 participants

submitted at least one questionnaire; 12 participants

responded at one measurement time point, 23

participants responded at two measurement time

points and 14 participants responded at each of the

three measurement time points. Table 2 shows the

mean values of the two subscales physical and mental

health for the three institutions at the three

measurement points. The corresponding mixed

models show a good model fit for physical health

(ICC1 = .76) and mental health (ICC1 = .53). In both

cases, the initial values of the participants vary

significantly (physical: t(67) = 26.37, p < .001;

mental: t(82) = 27.22, p < .001). Both physical health

(t(55) = 2.58, p = .013) and mental health (t(60) =

2.01, p = .049) increased over time, similar to the

individual findings of other studies on the use of

telerehabilitation for specific indications (Jaswal et

al., 2023; Eichler et al., 2019).

Table 2: Mean value of physical and psychological well-

being by measurement time and therapeutic facilities.

Total Onco. Cardio. Ortho.

Physical

(N)

41.27

(34)

42.90

(15)

37.18

(11)

43.83

(8)

Physical

(N)

45.21

(34)

49.72

(13)

42.50

(12)

42.28

(9)

Physical

(N)

45.96

(32)

46.99

(15)

45.35

(11)

44.49

(6)

Psychological

(N)

44.80

(34)

43.38

(15)

47.35

(11)

43.98

(8)

Psychological

(N)

47.49

(34)

43.35

(13)

51.79

(12)

47.74

(9)

Psychological

(N)

48.68

(32)

49.58

(15)

48.58

(11)

46.64

(6)

5.4 Subjective Assessment of the

Patients

After Van der Laan et al., 1997 usefulness and

satisfaction can describe tendencies of acceptance

and rejection on a cognitive and emotional level.

Figure 4 provides information on the ratings

according to medical indication in the value spectrum

-2 (low acceptance) to +2 (high acceptance). Both

dimensions are in the positive range of the scale. The

system was therefore rated as useful and satisfactory

and confirms the findings of Algarni et al., 2022

regarding the positive perception of telemedicine

treatments by patients. Usefulness was more

pronounced than satisfaction across all indications.

Between t1 and t2, the cardiology participants rated

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632

the system as more useful. Satisfaction increased in

cardiology but decreased in oncology and

orthopaedics.

Figure 4: Evaluation of usefulness and satisfaction at t1

(N=34) and t2 (N=32) by medical institution. The scale

ranges from -2 (low usefulness/satisfaction) to +2 (high

usefulness/satisfaction).

Another measure of usefulness in the value range 1

(low usefulness) to 5 (high usefulness) coupled with

usability (Venkatesh et al., 2003 & Davis et al., 1989)

produced a similar picture with positively assessed

usefulness. Usability was rated higher than usefulness

across all medical institutions. Effects between t1 and

t2 were negligible.

Of all questionnaire participants at t1 (N=34),

those who also completed the questionnaire at t2

(N=22) evaluated the system as more useful than

those who did not complete the t2 questionnaire

(N=12) (M = 1.02 vs. M = 0.48; t(32) = 2.08, p = .046

and M = 3.59 vs. M = 2.93; t(32) = 2.60, p = .014).

There were no significant differences between the

two groups in terms of satisfaction and usability.

5.5 Perspective of the Care Providers

The six interviews with the supervising therapists

(two in each medical institution), were used to discuss

(1) the care context (2) the expectations of and

towards patients and conflicts, (3) technologies in

everyday working life, (4) the introduction, everyday

use and evaluation of the quantified health system and

(5) the potential of digital care in the respective

rehabilitation setting. These results were described in

the evaluation report (Zöllick, 2023) and are

summarized below.

In an initial step, the patients' expectations were

compared with the services provided by the facility.

The summary of statements of medical care providers

involved in the project reflects the complex treatment

situation of lifestyle-changing therapy measures.

All participating therapeutic facilities use a

variety of technical devices in their daily work for

therapy and administration. Examples include digital

patient files, video calls as a method for consulting,

tablets and VR glasses. At the same time, therapists

emphasized the importance of the therapeutic hand as

an important tool. In addition to the benefits for

patients, the most important criterion for the use of

technology was emphasized as making work easier

for medical staff (Zöllick, 2023, p.17).

Therapists generally consider the easy usage and

operability as well as the reliability of the collected

data to be central to the acceptance of additional IT

systems in everyday treatment. These are the basic

prerequisites for therapists to provide patients with

valid feedback (Zöllick, 2023, p. 17f.), similar to the

findings of Wang et al., 2023 that the reliability of

data also plays a significant role in the acceptance of

telemedicine on the part of patients. Particularly

regarding the reliability of the data in the Quantified

Health system further potential for improvement has

been noted. Due to data protection requirements, the

project also involved duplication with other existing

IT systems, which required additional time. For

instance, the facility's own documentation system had

to be maintained in addition to the Quantified Health

PMS. Nevertheless, the advantages of the Quantified

Health PMS were emphasized as an additional source

of information and the possibility of monitoring and

closer interaction with patients (Zöllick, 2023, p.18).

The different expectations and approaches of

patients were identified as a potential source of

conflict with regard to their therapy and recovery

process. On the one side they range from the idea that

a massage works miracles, relieves pain and that a

certain amount of healing will occur over time with

the help of medication, up to those patients who want

to be informed precisely about their status and their

healing prognosis on the other side. The treatment

approach to patients must therefore be individualized

and sometimes insistent. From the perspective of care

givers patients are expected to be motivated to

participate and to reflect on how their own lifestyle

contributes to health problems (Zöllick, 2023, p.16).

Conflicts therefore arise on the one hand at the level

of patients' false expectations, which often result in a

lack of adherence. For some patients, retirement

requests can also prevent an active attitude towards

the rehabilitative measures (Zöllick, 2023, p.17).

These different expectations of the patient's

individual recovery process have an impact on

adherence: while adherence to treatment can still be

effectively ensured in an inpatient facility due to the

strict treatment plan, this appears to be much more

Quantiﬁed Health: A Feasibility Study on a Sensor-Based Feedback and Assistance System in Cardiology, Oncology and Orthopaedics

633

difficult for less adherent patients in an outpatient

setting. The therapists' limited personnel resources

also complicate the scheduling of appointments and

thus the flexible and intensive continuation of

therapeutic measures (Zöllick, 2023, p.17).

Nevertheless, checking the effectiveness of

training and therapy appears to be a motivator for

therapists when using additional, data-based therapy

systems, as the data can also be used to better explain

their effective training areas to patients (Zöllick,

2023, p.19). The future of digitalization in therapeutic

care is seen in the integration of different digital

documentation systems. From the therapists'

perspective, it would be desirable for all data, from

patient administration and service provision

documentation to the patients' medical parameters, to

be integrated into one system. Such a reliable,

integrated database would potentially reduce visits to

the doctor and facilitate therapeutic work. It would

also enable the transition between the inpatient and

outpatient sectors and between medical and

therapeutic therapy measures. Such a system also

offers potential for new healthcare professions, e.g.,

digitally supported prevention and rehabilitation

(Zöllick, 2023, p. 20).

6 DISCUSSIONS

Based on the results of the previous chapter, the

research questions initially posed in the project are

discussed below and lessons learned and

recommendations for action are derived from them.

The piloting of the Quantified Health system

corresponds to a feasibility study and was intended to

answer the question to which extent a telemedical

assistance systems can be used across indications and

sectors. Therefore, the added value and barriers,

particularly for patients but also for the care providers

involved, were examined. As mentioned above the

lessons learned described below are based on the

preparation for and discussions during the final event

at Fraunhofer FOKUS as well as the feasibility

evaluation by Charité Universitätsmedizin Berlin.

6.1 Different System Usage According

to Health Care Sector and

Professional Groups

Based on the feedback from the care providers

involved and the accompanying evaluation study, one

generic telemedical assistance and care systems can

generally be used across different indications.

For instance, in the outpatient sector, telemedical

assistance and care systems can be used primarily for

preventive purposes and to support therapy. Medical

professionals gain a deeper insight into the patient's

state of health and activities based on everyday and

training data. Based on these data they can adapt

therapy measures accordingly. Patients are more

strongly motivated (positive pressure) to exercise

adequately, i.e., sufficiently and in a controlled

manner. They gain additional therapy opportunities

alongside their conventional therapy.

In inpatient and rehabilitative area, telemedical

assistance and support systems can be especially used

in aftercare. This ensures regular contact between

therapists and patients, which contributes to the

sustainability of inpatient therapy measures. Since

aftercare does not necessarily have to be provided

following an inpatient stay, such systems can help to

close this gap.

The application of the system across different

sectors also showed that inpatient care represents a

good entry point for the use of such systems, as there

is longer-term contact with patients on site and

therefore more time for recruitment and referral to the

system. When care providers use the system, it should

be noted that different professional groups (sports

therapist, physiotherapist, doctor, study nurse) may

use the underlying system differently: Sports

physicians and sports scientists focus more on

training planning and training control,

physiotherapists pay close attention to the quality of

exercise execution, cardiologists primarily focus on

monitoring of vital signs.

6.2 Configurability for the Indications

Is Required

Nevertheless, the various application contexts in the

Quantified Health project, different needs of the

addressed indications (orthopedics, oncology,

cardiology) or sections (outpatient practices, inpatient

facilities) result in the need of different configuration

of data types and system functionalities. For example,

activity data (e.g., steps) and certain vital signs such

as pulse are required for all indications. However,

special values such as weight or the Borgscale

(current feeling of exertion) are used to varying

degrees in the individual indications. The same

applies to special system functionalities. For

example, everyday values are decisive in the

cardiology field, whereas the focus in the oncology

and orthopedic fields is on the training mode. In order

to keep telemedical assistance systems simple and

reduce complexity as much as possible, they should

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be configurable by the medical staff providing care

for the indication-specific use, so that certain

functions and the recording of special values can be

modularly adapted to the specific application. The

easy-to-understand aggregation of measurement and

therapy data in the form of a traffic light system, for

example, is essential for therapists and doctors.

6.3 Data Insight and Data Processing

Motivate both Patients and Care

Providers

Safety in everyday life can be restored and increased

using telemedical assistance and care systems. For a

particular group of patients, the use of telemedical

assistance and support systems increases motivation

to resume adequate physical activity. A certain self-

efficacy for the participants can be derived from this.

The patients' desire for a fine-grained insight into

their own health data and its progression (on a per-

day basis and statistically as an overview) was

established. However, it needs carefully to be

considered to which extent big volumes of available

personal health data can be meaningfully presented

on the sometimes very small displays of consumer

devices needs to be examined further. On the medical

side, optimizations of visualization (e.g., as a traffic

light system) and data aggregation can also increase

efficiency and thus make work easier for doctors and

therapists. Not every recorded health date is relevant

for the further course of treatment.

6.4 Integration or Connection to

Existing Systems Increases

Acceptance in Everyday Life

Naeemabadi et al., 2020 concluded that telemedical

assistance systems must be easy to use for patients. It

also turned out that, in addition to the desire for ease

of use and operability, there is also a need to combine

telemedical services with existing systems.

Fitness wristbands and smartwatches are often

already available in the consumer sector. Any further

additional component as a data source is less

desirable. This means that telemedical systems should

be developed in such a way that they can also

communicate with common components from the

consumer sector as a data source. On the medical side,

the various facilities already have a patient

management system (PMS). A redundant software

component for pure patient management is not

welcome, as it increases the documentation effort.

Instead, it should be possible to integrate the results

and recorded data of the additional digital health

application into the existing PMS. This means that

additional telemedical modules should be integrated

into the existing PMS via open easy-to-use interfaces.

6.5 Creation of New Jobs and

Professional Fields

The deployment and use of telemedical assistance and

feedback systems requires more attention from

physicians and therapists. This means that they

cannot be integrated into the daily workflow without

additional resources (time/staff). Telemedical

assistance and care systems contribute less to

increasing efficiency and resource optimization.

Rather, they improve the quality of therapy and

aftercare by creating added value for both patients

and healthcare professionals. Thus the use of such

systems results in additional costs for medical care. In

order to integrate telemedical therapy measures into

daily treatment routines an additional job or job

profile must be created, the so-called tele-therapist or

tele-physician. This professional position is

characterized by the fact that both technical skills and

medical expertise are required. Depending on the

indication and functionalities used in such systems

(e.g., video conferencing), psychological support

may also be required, as certain patient groups (e.g.,

oncology) not only require movement during

aftercare, but also make use of face-to-face

discussions with therapists.

During the project, it became obvious that a

private practice cannot offer digital care around the

clock. Due to the low volume of patients, it may not

be efficient in the outpatient sector to have one tele-

therapist per each care facility. In order to close this

gap in the health care system a flexible service

structure should be created, which can be provided by

a central service provider to be set up. Connected tele-

therapists or tele-physicians could support several

established medical practices via telemedicine

centers. The services offered via a center should

differentiate between a kind of on-call service (e.g.,

for the psychological care of oncological patients)

and selective support in everyday life (e.g., when

discussing therapy goals for cardiological or

orthopaedic patients).

7 CONCLUSIONS

In summary, the functions provided by the Quantified

Health system were sufficient for all indications.

Although commercially available systems (fitness

wristbands, smartwatches, PMS) also provide

Quantiﬁed Health: A Feasibility Study on a Sensor-Based Feedback and Assistance System in Cardiology, Oncology and Orthopaedics

635

separate functions, they do not yet act as an integrated

medical approved system from a technical

perspective. Regarding the target groups, however,

these functions must be weighted differently. For

example, an explicit training mode may not be

necessary in cardiology, as everyday values are

increasingly monitored during the process of

treatment. Configuration options should therefore be

integrated into the Quantified Health App or PMS in

order to adapt the system to the respective indication

and the media skills of the end user.

In principle, the acceptance of the Quantified

Health system was given among end users. Future

applications of the system are possible in the area of

prevention (e.g., to control high blood pressure), after

an acute event (e.g., in inpatient rehabilitation or

aftercare) but also for treatment of chronic diseases in

order to modulate lifestyle in everyday life. However,

a certain degree of affinity with technology was a

prerequisite for the use of the Quantified Health

system. It became clear that the large number of

functions provided can lead to users being

overwhelmed by the possible system operations.

Especially in inpatient aftercare, the additional

support in everyday life is perceived as a benefit for

patients and fills a gap in the healthcare system. The

acute event is often a starting point for lifestyle-

changing activities following inpatient treatment.

Constant contact with the doctor or therapist increases

the patient's motivation. In the outpatient setting,

especially for chronic patients, this process is much

more protracted and sometimes problematic, because

behavior patterns that have been practiced over long

term have to be changed.

The project also revealed that an additional digital

system would be difficult to integrate into existing

workflows under the current working conditions.

New tele-workstations for tele-doctors and tele-

therapists should be created here. The care providers

were consistently positive about the fact that they

would receive more data from patients' everyday lives

and that the improved data situation could also

provide motivation for the sustainable care of

patients.

Integrated solutions with well-bridged interfaces

in particular allow medical care providers and

patients to supplement prevention and rehabilitation

measures and therefore have considerable social and

economic potential. The project results of Quantified

Health can be used to develop a reference architecture

(description of the technical components and process

recommendations) for digital aftercare in everyday

life for the indications of cardiology, oncology and

orthopedics. Initial concepts for a standardized, cross-

indication open telehealth platform already exist.

In order to be able to define business models, the

next step would be to determine not only the targeted,

medical examination of effectiveness but also the

economic efficiency. Business models can then be

derived from these results. Future work on cross-

indication teleassistance systems should therefore

focus on

• Technical adaptation and optimization in

accordance with the lessons learned and

recommendations for action,

• the audit of effectiveness and efficiency and

• the requirements for inclusion in standard care

by providing an accounting code.

ACKNOWLEDGEMENTS

This work was supported by the project DiBeA

funded by the German Federal Ministry of Health

based on a decision of the German Federal Parliament

(Bundestag) under funding ID ZMI1-2521TEL20A.

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