Kraushaar Paper2 Scribd

8/3/2019 Kraushaar Paper2 Scribd

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Title:1

Engaging self-correcting feedback control to increase physical activity and reduce bodyweight2

and disease risk in overweight sedentary adults.3

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Running Title: Self-correcting feedback control for weight loss5

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Authors:7

Kraushaar Lutz Erwin*, MSc., Department of Public Health Medicine,8

School of Public Health, University of Bielefeld, POB 100131, 33501 Bielefeld,9

Germany, [email protected]

Krmer Alexander, M.D., Ph.D., Professor and Head, Department of Public Health Medicine,11

School of Public Health, University of Bielefeld, POB 100131, 33501 Bielefeld,12

Germany13

* Corresponding author14

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Date: 03/201016

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This study was supported by Siemens Betriebskrankenkasse (SBK), Siemensallee 84, 7618718

Karlsruhe, Germany19


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Abstract20

Objective: To investigate whether engaging web-enabled cognitive feedback control over the21

introduction of leisure time physical activity (LTPA) will yield adoption of health enhancing LTPA22

volumes among sedentary, overweight adults, and promote clinically relevant improvements of23

anthropometric, metabolic and fitness-related vital signs. Design: Longitudinal LTPA intervention24

study, commencing with a minimum weekly requirement of 3x20 minutes of high-intensity interval25

training (HIT), and requirement for web-based self-monitoring andreporting of LTPA volume and26

body weight. Subjects: 83 overweight, sedentary, otherwise healthy adults (age 26-68y, BMI 25.1-.27

41.7 kg/m2, 24% female). Measurements: Anthropometric parameters, body fat (phase sensitive28

multi-frequency BIA), total-to-HDL cholesterol ratio, VO2peak (cardiopulmonary exercise testing,29

CPET), self-reported time spent for LTPA, frequency and latency of use of the web-enabled tool.30

Results: At 24-week follow-up, substantial voluntary increase of time spent for LTPA (mean and31

median of 135 and 170 minutes per week respectively) in the group of 72% of participants who32

successfully engaged cognitive feedback control (CFG), vs. no increase in the remaining participants33

who served as the control group (CG). CFG witnessed significantly improved peak oxygen34

consumption >1 metabolic equivalent (MET) vs. no improvement in CG. CFG also reduced BMI, body35

weight, body fat and TCH/HDL by 1.6 kg/m2

, 4.8 kg, 3.6 kg and 0.25 respectively in CFG vs. 0.436

kg/m2, 1.4 kg, 1.1 kg and an increase in TCH/HDL ratio (0.16) in CG. Conclusion: Engaging self-37

correcting feedback via internet-based self-monitoring and feedback control may be a promising38

strategy for instituting sustainable health enhancing behavior change in overweight adults, offering the39

possibility of open-ended intervention delivery at low costs.40

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Key words: Obesity, Physical Activity, Individual Behavior, Homeostasis, Feedback42

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Background44

Excess body weight causally contributes to the development of cardiometabolic disease [1]. That45

leaves the question how to institute lasting weight loss and weight maintenance in individuals whose46

physical activity and dietary habits have promoted the onset of overweight and obesity. Lifestyle47

interventions, which target participants deficits in LTPA and their surfeits in caloric intake, may48

perform well in initiating weight loss and improvements of disease risk. However, an almost complete49

reversal to baseline status within a 3-5 years post-intervention period has been observed [2, 3], as well50

as a 95% failure rate of dieters attempts at losing weight and maintaining weight loss in the long term 51

[4]. These data suggest that a sustainable public health strategy for health behavior change has yet to52

be found. To this end we developed a biobehavioral model with which to explain the observation of53

runaway weight gain in our society, and from which to formulate a testable hypothesis for sustainable54

remedial intervention. Since intervention efficiency for public health is our objective, we desired the55

intervention to allow for optimum reach into the at-risk population under the given economic, regulatory56

and resource constraints which define the local German health care system.57

The biobehavioral origin of the obesity epidemic58

The ecological observations guiding the development of our hypothesis were (a) the absence of59

excess body weight in human societies of hunter/gatherers living in their natural habitat [5, 6], (b) the60

obligatory physical activity cost for food acquisition in this habitat [6, 7], and (c) mans progressive61

weight gain secondary to the abolition of obligatory energy expenditure [7] and the introduction of62

processed foods of high energy density in modern society. We posit that the latter derails the anabolic63

and catabolic constituents of a negative feedback loop, which autonomously controls energy64

homeostasis. Figure 1 presents our proposed feedback loop model, of which three essential aspects65

warrant further elaboration. Firstly, the model positions catabolic foraging and anabolic feeding as the66

inextricable appetitive and consummatory components of ingestive behavior (AIB & CIB) [8, 9]. Their67

underlying neurohormonal pathways autonomously establish energy homeostasis through negative68

feedback control [10]. The hormones neuropeptide Y (NPY) and leptin have emerged as the chief69

stimulator and moderator respectively of an organisms drive to acquire food [11-14], which has been70

found to operate with remarkable similarity in all vertebrates and even in some fish [15, 16].71

Secondly, there is dopamine as the neural substance that actuates the wanting for hedonic72

experiences, which an organism has learned to associate with certain stimulants [17]. Sweet and fatty73

tastes are such stimulants which fuel mans dopaminergic drive [18] and consequently his cravings for74


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sweet and fatty foods [19]. The drive to preferentially select such comestibles and their caloric content75

may have carried a distinct survival value for an organism subsisting in a habitat which is76

characterized by volatile food supplies and constant demands for physical activity. In modern human77

society however, the addictive power of typically sugar- and fat-enriched processed foods conspires78

with the abolished need for physical activity to derail energy homeostasis and its underlying79

neurohormonal system of negative feedback control.80

A functioning negative feedback control implies that once an individuals drive to forage is activated,81

and food reward is presented free of the energy cost of preceding PA, the eventual strengthening of82

the leptin signal, secondary to an accumulation of energy reserves, would moderate any subsequent83

motivation to forage when the latter is initiated by energy flux activated NPY signaling. However, at84

least two conditions have been observed which potentially derail negative feedback control. First,85

there is leptin resistance, a condition frequently encountered in overweight individuals [20]. Second,86

there is conditioned potentiation of feeding, a variant of classical conditioning, in which a previously87

unrelated stimulus, when paired with food presentation, arouses the dopamine driven wanting of food88

when the then conditioned cue and access to food are paired subsequently [21]. Once stimuli, such as89

time of day, physical location or presence of others have been conditioned into cues for food intake, a90

dopaminergic drive is engaged to eat in excess of physiological need.91

In our model, the controlled parameter of negative feedback is energy adequacy under the given92

environmental constraints, rather than bodyweight, as suggested by set-point theory [22]. The latter is93

challengeable on observational and evolutionary grounds. Its prediction of eventual weight stability94

fails to reconcile with the observation of longitudinally increasing bodyweights of societies and of95

individuals. The ability to cap body weight would have increased inclusive fitness only if environmental96

conditions had facilitated pathological weight increase to the point of affecting an individuals chances97

to reproduce and survive. This is hardly reconcilable with our current understanding of the scarcity and98

volatility of food supplies that has characterized the hominid environment throughout evolution.99

In defense of the theory, the failure of set-point control has been blamed on a purely100

cognitive/executive decision-override of homeostatic body weight control [23], which amounts to101

blaming the overweight individual for his predicament.102

Contrary to this view, our model exonerates the overweight individual, as it suggests his behavior to be103

driven by evolutionary conditioned neurohormonal mechanisms, which are autonomous in nature [10]104

and maladapted to the challenges of the modern environment.105


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In this context, voluntary LTPA offers itself as a cognitively controllable means to correct the energy106

imbalance and metabolic consequences, which result from the absence of the obligatory physical107

activity cost of food in the industrialized human society. In support of this view, LTPA has been108

presented as the critical component of sustained weight loss in the long-term follow-up of successful109

weight reducers [24, 25]. However, interventions aimed at increasing LTPA suffer from high attrition110

rates of typically 50% within the first 6 months [26-28]. As an explanation we offer (a) failure to111

consider participants time constraints, and (b) failure to engage feedback control. With respect to time112

constraints, lack of time is sedentary individuals most frequently cited obstacle to the cultivation of a113

regular exercise habit [29-31]. This real or perceived lack of time emerges relative to current114

recommendations, such as those of the American College of Sports Medicine [32] or the Institute of115

Medicine [33] who advocate 150-250 minutes per week and 60 minutes per day respectively of health116

enhancing physical activity (HEPA). Failure to overcome the discrepancy between time required and117

time perceived available for HEPA may inadvertently either freeze individuals into their sedentary118

habits, or promote recidivism from attempts at adopting HEPA.119

With respect to cognitive feedback control, its sine qua nonis self-monitoring, which, when practiced,120

has been found to significantly improve adherence to behavior change [34].121

The hypothesis122

Taken together, the observations discussed above led to our hypothesis that engaging self-monitoring-123

based feedback control over an initially minimal but acceptable LTPA volume will promote the124

voluntary adoption of progressively increasing PA volumes in previously sedentary overweight adults,125

who self-selected for participation in a weightloss intervention. We further hypothesized that self-126

monitored and self-reported PA volume will correlate with objectively measurable vital signs of body127

weight and physical fitness.128

Overcoming the economic obstacles to effective lifestyle change129

The high costs of evidence-based lifestyle interventions, health care providers time limitations and a130

lack of reimbursement for health care providers preventive services constitute substantial barriers to131

the provision of preventive lifestyle change interventions [35]. In Germany, legislation mandates health132

insurers to subsidize, but not fully reimburse, members voluntary participation in selected exercise133

and diet programs, up to an annual ceiling. However, utilization of these funds typically is neither134

targeted to the beneficiarys health profile nor is it informed by medical advice or guidance. This135

strategy renders the preventive efforts efficiency probably sub-optimal. An internet-based intervention,136


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which is (a) targeted to an individuals health profile, which (b) automates, standardizes and maintains137

the process of engaging feedback control over HEPA, and which (c) integrates into the statutory and138

economic landscape of the health care system could constitute an economically viable and evidence-139

based alternative. To this end we consulted with a medium-sized statutory health insurance provider140

with the aim of operationalizing the intervention to meet with the acceptance of the primary cost carrier141

of health care services in Germany.At a 12.- monthly deductible, to be contributed out-of-pocket by142

the participants, the insurer considered the proposed intervention to satisfy economic and statutory143

constraints, and subsequently agreed to its realization as a pilot project within the community setting of144

an industrial estate in the South-Western German city of Karlsruhe. The 12.- out-of-pocket145

benchmark had emerged from an evaluation of primary care patients willingness-to-pay for preventive146

services, conducted in a primary-care setting of the close-by community of Heidelberg [36]. Of the 967147

survey respondents (99.4% response rate) recruited from among 5 primary care practices, 27%, 40%148

and 12% had indicated willingness-to-pay less than 15, 15 -40, and >40 respectively for149

preventive services.150

Methods151

The intervention was designed as a non-randomized controlled trial in sedentary and overweight,152

apparently healthy adult men and women. The study protocol conforms to the ethical guidelines of the153

1975 Declaration of Helsinki. Approval was obtained from the ethics committee of the state medical154

board of Baden-Wrttemberg. All participants gave written informed consent prior to enrollment.155

Subjects156

Subjects were recruited from among 200 German holders of a compulsory health insurance policy who157

had taken up their insurers invitation to participate in a subsidized fitness and physical activity158

examination. All subjects were employees at a large industrial estate of a multinational German159

electronics manufacturer. Figure 2 presents an overview of the recruitment process.160

Inclusion criteria were a self-reported current volume of LTPA of 1 hour or less per week and a body161

mass index (BMI) in excess of 25 kg/m2.162

Exclusion criteria were known diseases and physical disabilities preventive of participation in an163

exercise program. All participants were Caucasians of German extraction.164

Intervention165

The minimum requirement for all participants was an unsupervised exercise protocol of HIT of thrice166

weekly 20 minutes (either running or cycling) in line with U.S. government recommendations,167


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published under the Healthy People 2010 initiative, and calling for moderate-intensity exercise of at168

least 30 minutes on at least 5 days per week, or alternatively, for 20 minutes high-intensity exercise at169

least thrice weekly [37]. Thrice weekly 15-minute HIT bouts have shown to yield significant170

improvements of parameters of metabolism and exercise capacity [38].171

Each 20-minute HIT session was to consist of 4 repeated 60-s sprints at a heart rate commensurate172

with 85% to 95% of participants individual VO2peak with a 4-min recovery phase between sprints.173

During recovery, subjects were to continue their mode of exercise at an intensity level commensurate174

with their anaerobic threshold. Subjects were instructed not to perform HIT on consecutive days, but175

were encouraged to additionally engage in moderate-intensity endurance training at 95-115% of their176

individual anaerobic threshold.177

All participants who opted for the use of heart rate monitors during exercise were given target heart178

rates for the HIT exercises and recommendations for the optimal heart rates during continuous aerobic179

exercise. All heart rate recommendations were based on the individuals cardiopulmonary exercise180

test results. Participants who decided against the use of heart rate monitors were familiarized with the181

use of the 10-point OMNI rating scale of perceived exertion [39] and instructed to perform the high-182

intensity intervals at an approximate rating of 8 and the recovery phase at a rating of 5-6. The OMNI183

scale has been validated for use in equivalent populations [40, 41].184

Self-Monitoring185

To facilitate self-monitoring, and the supervision thereof by the investigator, an electronic lifestyle file186

(ELF) was created into which participants were to report their actual time spent on exercise and their187

bodyweight. The ELF facilitates a 6-weeks cumulative graphical display of actual vs. target values.188

Target performance for the initial 6-weeks period was based on the weekly 60-minutes HIT protocol.189

Upon completion of each 6-weeks period the target for the following 6 weeks was set to increase by190

10% over the actual volume reported for the preceding 6-week period. Participants were encouraged191

to log their actual PA performance and their bodyweight on a daily basis, either by direct access to192

their secured web-page or through an applet installed on their mobile phone facilitating SMS-based193

reporting of PA and bodyweight.194

Measurements195

Body weight and standing height were measured in light sports clothing and without shoes to the196

nearest 0.1 kg and 1 cm, using a wall-mounted anthropometer and a calibrated electronic scale,197

respectively. BMI was calculated as the ratio between weight and height squared (kg/m2).198


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Body composition was measured using an impedance analyzer device and software (BIA 2000-S,199

Data Input, Frankfurt, Germany) for tetrapolar bioelectrical impedance analysis (BIA) measurement of200

resistance (R) and reactance (Xc) at frequencies of 5, 50 and 100 kHz. Measurements were made at201

the right side of the subject between the wrist and ankle while in a supine position and after having202

rested for 5 minutes. The equipment, analytic algorithms and the measurement protocol have been203

validated previously in comparable populations [42, 43].204

Exercise testing was performed as cardiopulmonary exercise test on a cycle ergometer (Customed,205

Germany) using a ramp protocol to exhaustion with the ramp increment chosen, based on age, weight,206

height and training history, as to reach exhaustion within 8 to 12 minutes [44]. For the first 3 minutes207

the workload was fixed at 5 W.208

The resistance on the cycle ergometer was controlled by the ergospirometric software (Cortex,209

Leipzig, Germany) to be independent of pedal cadence.210

Spiroergometry was carried out using a breath-by-breath-system (Cortex MetaLyzer 3B, Leipzig,211

Germany), which has been validated previously [45]. Expired air was collected continuously using a212

facemask. The system was calibrated prior to each test in accordance with manufacturers guidelines213

using a 3-L syringe for volume calibration and ambient air measure for gas calibration.214

During all tests, heart rate was recorded with a wireless chest strap telemetry system (Polar, Kempele,215

Finland). Simultaneous gas exchange measurements consisted of minute ventilation (VE), oxygen216

uptake (VO2; electrochemical cell), and carbon dioxide output (VCO2; infrared analyzer). For217

calculations, data were averaged over every 20 seconds.218

Peak oxygen uptake (VO2peak) was defined as the highest value for oxygen uptake averaged over 20219

seconds.220

Venous blood was sampled in EDTA tubes in the morning between 07:30 and 08:45 after an overnight221

fast. Total and HDL cholesterol were determined by standard laboratory methods using certified222

assays in a local clinical laboratory.223

All analyses performed at baseline were repeated at follow-up.224

Adherence Definition225

Adherence was defined as meeting the minimum criteria of having recorded a minimum weekly226

duration of endurance exercise (volume aspect of adherence) of 60 minutes (3 x 20 minutes of HIT) or227

more for at least 12 consecutive weeks (duration aspect of adherence), with the last self-reported login228

not earlier than 1 week (latency aspect of adherence) prior to the date of final assessment. With229


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physical fitness being the primary and measurable vital sign, the 12-weeks duration is in keeping with230

published evidence, which suggests that measurable effects accrue to VO2peak after such durations231

[46], with decay of the effect being observable within 14 days of discontinuation of the exercise232

regimen [47]. Participants who did not meet the adherence criteria were considered the control group.233

Statistical Analyses234

Prior to the study, we performed power and sample-size calculations, with both calculations based on235

a hypothesized ratio between adherent and non-adherent participants r=3. To achieve a power of236

90%, we needed 50 participants to detect a between-group difference for VO2peak of 1 MET (3.5237

ml/kg/min) and for BMI of 1 kg/m2. Analyses for differences between groups at baseline were238

performed using t-tests. Changes from baseline to follow-up were tested using paired t-test for within-239

group changes, and unpaired t-tests for differences of changes between groups. Statistical240

significance was accepted at p


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VO2peak of 2.4 ml/kgLBM/min, CFG had significantly increased VO2peak by 6.2 ml/kgLBM/min, with261

p-value for within- and between-groups of 3, with a significance for this between-group difference at p


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a web-based self-monitoring tool, to institute physical activity in previously sedentary and overweight292

adults. The results of this study support the hypothesis that by engaging self-correcting feedback, with293

weight-loss being its objective, users voluntarily and significantly increase their physical activity294

volume over an initially prescribed minimum. To start with a minimum prescription may be an295

important strategy to overcome sedentary individuals perceived gap between time required and time296

available for HEPA. Engaging a cognitively controlled self-correcting feedback loop may therefore help297

previously sedentary and overweight adults to voluntarily, gradually and substantially increase their298

physical activity volume over an initially prescribed minimum, resulting in significant reductions of body299

weight and associated disease risk. A call has been made very recently for the EU to develop national300

physical activity recommendations along the new guidelines formulated by the U.S. American Heart301

Association (AHA) and the American College of Sports Medicine (ACSM) [54]. These guidelines302

specifically acknowledge the evidence-based need for all healthy adults aged 18-65 to perform either303

moderate-intensity aerobic exercise for a minimum of 30 min five times weekly, or 20 min of vigorous304

exercise 3 times weekly or any combination thereof [55]. This interventions starting point was a305

CPET-based heart rate-controlled exercise recommendation for an initial 3x20 minutes HIT routine.306

Individualizing an exercise prescription to a participants personal cardiopulmonary dynamics307

maximizes the training effect. The hoped-for consequence is that a perceivably improved performance308

powerfully promotes continued adherence.309

Letting participants self-determine their PA volume, given the initial weekly requirement of 3x20310

minutes of HIT, yielded a substantially larger actually performed volume of exercise. It is therefore311

tempting to suggest, that it may not so much be participants fidelity to a pre-conceived one-size-fits-all312

exercise curriculum, which lifestyle change program providers should be concerned with. Rather313

should we focus on getting people to commit to and commence with a physical activity habit, which, if314

prescribed individually to yield some quick tangible effect, will develop its own momentum. It is for315

future research to contrast these two strategies under the a priorihypothesis of a significant difference316

in outcome.317

In this study, the term adherence does not relate to the volume or duration of PA, but to the presence318

of a cognitively controlled feedback loop at the end of a 6-months observation period. This constitutes319

a small, but substantially different way of defining adherence, which emerges from the theory and320

model underlying this intervention strategy. Its latency aspect differentiates it from adherence321

definitions, which are exclusively based on any combination of percentages of volume, of duration or322


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of attendance. It provides an answer to the question, what proportion of study participants currently323

adheres to the PA protocol and has done so for durations and at PA volumes, which are expected to324

yield tangible health benefits. Hence, the latency aspect is essential for determining how successful an325

intervention has been at releasing its participants with a modified health habit. With 72% of the326

participants meeting this criterion, the intervention compares favorably with the 55% (CI 0.39-0.72)327

adherence rate achieved in a comparable worksite intervention of a 24-weeks PA program consisting328

of 3 weekly 20-minutes high intensity aerobic workouts in addition to strength training [56], and with329

the 60% adherence rates typically reported for PA interventions. This difference in adherence is330

significant, two-tailed, at p(z=2.28)5 years, have found self-337

monitoring, a high level of PA and a low-calorie-low-fat diet as the three most important determinants338

of weight loss and maintenance of reduced body weight [25, 57].339

Strengths and Limitations340

One major strength of our study is its simulation of a real-life implementation, designed to be341

economically acceptable to the provider of statutory health insurance under which 90% of the resident342

population is covered. Another notable strength is the studys objective measurement of changes in343

body weight, body composition, TCH/HDL ratio and physical fitness resulting from an internet-344

delivered intervention which was designed to engage cognitive feedback control over physical activity345

behavior.346

The primary limitation of our study is that (a) these results represent initial improvements of weight and347

fitness status, and that (b) the study design, which necessitated participants out-of-pocket348

contributions with the objective of simulating a future real-life implementation, prevented us from349

randomizing participants into a control and intervention group. The resulting selection of non-adherent350

participants as the control group may have led to selection bias. However, there were no significant351

between-group differences of any of the parameters measured at baseline. Expressed as Cohens d,352

the differences in parameters at baseline remained at d0.1, with the exception of age, the difference353


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of which had a Cohens d=0.3. Conventionally, effect sizes are considered small, medium and354

large fordvalues of 0.2, 0.5 and 0.8 respectively [58]. Also, self-report of PA is inherently subject to355

bias. Since the self-reporting technique did not enable participants to differentiate between levels of356

exercise intensity, the calorific equivalent of the reported exercise volume cannot be determined.357

Hence, no dose-response relationship could be established between PA volume and outcome. Also,358

subjects were free to record either daily or as and when PA was performed. Enforcing daily login might359

have reduced the number of subjects who failed to engage a cognitively controlled feedback loop. In a360

follow-up trial we are encouraging participants to perform daily monitoring not only of body weight and361

PA but also of dietary intake. We hypothesize that daily feedback will significantly improve adherence362

and outcome.363

Based on this hypothesis, the ELF is being further developed to include dietary monitoring and to364

facilitate telemetric monitoring of PA volume and intensity as well as telemetric monitoring of body365

weight, blood pressure, blood glucose and ECG. We encourage fellow researchers to avail366

themselves to this tool, and welcome all enquiries related to academic research.367

Conclusion368

This study demonstrates that a web-enabled engagement of cognitive feedback control enables369

sedentary and overweight individuals to voluntarily increase LTPA to yield clinically relevant370

improvements of anthropometric, metabolic and fitness related vital signs. The internet-enabled371

implementation provides for a low-cost open-ended intervention delivery to large at-risk groups,372

possibly facilitating sustainable improvements of health behaviors. Follow-up research should373

elucidate the determinants of sustainability and efficiency within the statutory and economic374

constraints of the given health care system.375

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Contributors378

L.E.K. contributed to the design of the study, collection and assembly of the data, analysis and379

interpretation of data and drafting the article. A.K. took part in the interpretation of data and drafting of380

the article. All authors approved the final manuscript. L.E.K. accepts full responsibility for conducting381

the study.382

383

Funding384

The study was supported by Siemens Betriebskrankenkasse (SBK).385

386

Acknowledgement387

We thank all participants who took part in this study.388

389

Conflict of Interest390

All authors declared to have no conflict of interest.391


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Table 1:Physiological and Anthropometric Characteristics of the Participants530

Baseline ChangeMean SD Mean 95% CI

Age1 (years)non-adherent (N=22) 48 8 - -

adherent (N=60) 51 9 - -

Gender1 (% female)non-adherent (N=22) 23 - -

adherent (N=60) 25 - -

VO2peak1 per kg body weight(ml/kg/min)

non-adherent (N=22) 32.2 8.01 -0.59 (-0.36 to +1.54)adherent (N=60) 32.3 8.10 +3.732; 4 (+2.71 to +4.74)

VO2peak1 per kg lean body mass(ml/kg/min)

non-adherent (N=22) 84.6 14.2 -2.4 (-5.0 to +0.3)adherent (N=60) 86.3 16.6 +6.22; 4 (+3.8 to +8.5)

TCH/HDL1non-adherent (N=22) 4.10 1.12 +0.16 (-0.13 to +0.46)

adherent (N=58) 4.42 1.06 -0.252;3 (-0.38 to -0.11)

BMI1 (kg/m2)non-adherent (N=22) 29.7 3.7 -0.4 (0 to -0.8)

adherent (N=60) 29.8 3.5 -1.62;4 (-1.1 to -2.0)

Body Weight1 (kg)non-adherent (N=22) 92.6 13.1 -1.42 (-0.1 to -1.6)

adherent (N=60) 91.6 13.2 -4.82; 4 (-3.5 to -6.2)

Body Fat1 (kg)non-adherent (N=22) 27.5 9.0 -1.12 (-0.1 to -2.0)

adherent (N=60) 27.5 8.4 -3.62; 4 (-2.6 to -4.7)

Abbreviations: VO2peak = Peak Oxygen Consumption; TCH = total cholesterol; HDL = high-density lipoprotein cholesterol531

Data are mean SD unless otherwise specified.532

1: p>0.05 for between-group difference at baseline; 2: significant difference from baseline to follow-up at p


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537

538

Fig.1. Negative Feedback Loop of Energy Homeostasis539

NPY = Neuropeptide Y540

541

542


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543

Fig. 2. Flowchart Recruitment544

545


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Fig. 3. Changes of vital parameters at 24-weeks follow-up. P-values centered in each bar refer to547

within-group changes from baseline to follow-up. P-values between the bars indicate significant548

between-group differences.549

550

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