Positive energy balance and reduced physical activity are common reasons for weight gain [
In previous uncontrolled trials we had already evaluated the efficacy of a telemedical mental motivation program [
We, therefore, tested in the present randomized controlled study the hypothesis that (i) a telemedical intervention with or without telemedical coaching leads to long-term weight losses and other beneficial clinical outcomes and (ii) whether telemedical coaching shows an additional impact on the results in overweight participants in an occupational health care setting.
In an occupational health care setting overweight employees of the companies “
Participants in the telemedical (TM) and telemedical coaching (TMC) group were equipped with telemetric scales (smartLAB scale W; HMM Holding AG, Dossenheim, Germany) and pedometer (smartLAB walk P+; HMM Holding AG, Dossenheim, Germany) automatically transferring recorded data into a personalized online portal. These data could be monitored from both, participant and the coaching team of the WDGZ. Participants in the TM group could monitor their body weight and steps (daily, weekly or average) but got no further support during the 12 weeks of intervention. The TMC group got additionally weekly care calls from trained coaches. These care calls included information about overweight or obesity-related diseases like type 2 diabetes, healthy diet, physical activity, and coping strategies for lifestyle changes. Moreover, acquired data were discussed (i.e., steps and weight) and participants were further motivated to achieve their individual goals (i.e., weight goals and healthy lifestyle changes) using a mental motivation program [
At baseline and after 12 weeks of intervention participants visited their medical corporate department for determination of anthropometric and clinical data (i.e., age, sex, body weight, height, BMI, waist circumference, blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides, and hemoglobin A1c (HbA1c)). The assessors were blinded for group allocation. Body weight was measured in light clothing to the closest 0.1 kg, height to the closest 0.5 cm, and waist circumferences at the minimum abdominal girth (midway between the rib cage and the iliac crest). Blood pressure was measured after a five-minute rest in a sitting position on both arms. Venous blood was collected by inserting an intravenous cannula into the forearm vein and laboratory parameters were analyzed at the local laboratory. One year after the end of the intervention weight data out of the online portal were used for the follow-up analysis. These weight data were continuously recorded during the follow-up period and automatically transferred to the online portal by the scales. Afterwards, the online portal was closed after the 12-month follow-up.
Primary endpoint was the reduction of body weight after 12 weeks of intervention and its later course during the 12-month follow-up compared between all of the three groups. Secondary endpoints were the changes in BMI, waist circumference, systolic and diastolic blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and HbA1c after 12 weeks of intervention. Sample size had been calculated assuming that telemedical coaching might affect body weight. Our data indicated that due to telemedical lifestyle intervention a reduction of 2.3 kg in body weight in the TMC group can be assumed, while for the control group a reduction of only 1.0 kg was estimated. To be able to measure such a difference with a power of 90% and a level of significance of 5%, at least 50 datasets per group were needed. Since a dropout rate of 20% was estimated, the plan was to recruit 60 subjects per group, i.e., a total of 180 persons. Intention-to-treat analyses were performed. Missing values were substituted by the “last-observation-carried-forward” principle. Means ± standard deviations or standard error of means are shown, as appropriate. Baseline differences had been analyzed by using the Chi square test or Kruskal-Wallis test for nonparametric data and the ANOVA test for parametric data. The Wilcoxon signed-rank test was used for the analysis of differences within all the groups. The Kruskal-Wallis statistics with Dunn’s multiple comparisons test was conducted for the comparison of Δ-values. The Friedman test with Dunn’s multiple comparisons test was used to test the within group differences between time points. The Bonferroni correction was applied to adjust for multiple testing. Level of significance was set at p=0.05. Statistical analyses were performed using GraphPad Prism 6.04 (GraphPad Software, San Diego, CA, USA) and SAS statistical package version 9.3 (SAS Institute, Cary, NC, USA).
Fifty-eight participants were randomized to the TMC group, 61 to the TM group, and 61 to the control group (Figure
Study population characteristics.
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Sex (male /female) [%] | 52 / 48 | 39 / 61 | 44 / 56 | ||||||
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Age [years] | 44 ± 10 | 45 ± 10 | 47 ± 10 | ||||||
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Weight [kg] | 98.9 ± 18.7 |
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97.9 ± 17.4 |
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-1.9 ± 4.0 | 101.7 ± 17.7 | 100.8 ± 17.8 | -0.8 ± 3.1 |
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Body Mass Index [kg/m2] | 32.7 ± 4.6 |
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32.9 ± 4.3 |
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-0.6 ± 1.3 | 34.0 ± 5.3 | 33.8 ± 5.4 | -0.3 ± 1.0 |
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Waist circumference [cm] | 109 ± 12 |
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-3 ± 6 | 109 ± 13 |
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-2 ± 4 | 112 ± 13 | 111 ± 14 |
-2 ± 5 |
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Systolic blood pressure [mmHg] | 141 ± 24 | 138 ± 24 |
-4 ± 17 | 141 ± 23 | 139 ± 24 | -3 ± 17 | 141 ± 17 | 140 ± 19 | -1 ± 17 |
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Diastolic blood pressure [mmHg] | 90 ± 13 | 86 ± 12 |
-4 ± 11 | 88 ± 14 | 88 ± 13 | 0 ± 10 | 89 ± 12 | 88 ± 11 | -1 ± 12 |
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Triglycerides [mg/dl] 1 | 191 ± 120 | 169 ± 94 | -22 ± 77 | 167 ± 83 | 169 ± 153 | -5 ± 115 | 191 ± 119 | 181 ± 127 | -10 ± 62 |
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Total cholesterol [mg/dl] 1 | 218 ± 33 | 207 ± 33 |
-10 ± 28 | 210 ± 37 | 204 ± 36 |
-5 ± 15 | 225 ± 44 | 226 ± 46 | 1 ± 22 |
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HDL cholesterol [mg/dl] 1 | 51 ± 15 | 53 ± 15 |
2 ± 7 | 51 ± 14 | 52 ± 13 | 1 ± 6 | 54 ± 18 | 55 ± 22 | 1 ± 11 |
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LDL cholesterol [mg/dl] 1 | 135 ± 28 |
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-5 ± 22 | 131 ± 31 |
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-5 ± 14 | 140 ± 39 | 141 ± 41 | 1 ± 18 |
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HbA1c [%] 1 | 5.6 ± 0.3 | 5.5 ± 0.3 |
-0.1 ± 0.2 | 5.6 ± 0.3 | 5.5 ± 0.3 | 0.0 ± 0.1 | 5.7 ± 0.5 | 5.7 ± 0.4 | 0.0 ± 0.3 |
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HbA1c 5.7-6.4% [%] | 28 | 21 | 7 | 39 | 33 | 6 | 43 | 43 | 0 |
Shown are means ± standard deviations. Baseline differences had been analyzed by using the Chi square test and the ANOVA test. The Wilcoxon signed rank test was conducted for analysis of differences within all of the groups (
Distribution of BMI categories between groups at baseline.
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19 (32.8%) | 17 (27.9%) | 14 (23.0%) |
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20 (34.5%) | 26 (42.6%) | 25 (41.0%) |
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16 (27.6%) | 13 (21.3%) | 13 (21.3%) |
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3 (5.2%) | 5 (8.2%) | 9 (14.8%) |
TMC, telemedical coaching group; TM, telemedical group. Frequency of BMI categories was not different between all groups.
Flow diagram.
Participants of the TMC (98.9 ± 18.7 kg to 95.8 ± 16.9 kg (-3.1 ± 4.8 kg; p<0.0001) and the TM group (97.9 ± 17.4 kg to 96.0 ± 16.7 kg (-1.9 ± 4.0 kg; p=0.0012)) significantly reduced weight (Table
Follow-up analysis of body weight demonstrated that participants of both intervention groups were further able to reduce weight after 12 months (Figure
In the present randomized controlled three-armed study we could show that telemedical-based lifestyle interventions are applicable to motivate overweight individuals for lifestyle changes resulting in long-term weight reductions. In particular, the combination of telemedical devices and telemedical coaching led to greater reductions in body weight as well as improvements in cardiometabolic risk factors.
Other studies with different cohorts (e.g., persons with serious mental illness or obese patients with at least one cardiovascular risk factor) confirm our results and demonstrate that telemedical coaching or telemonitoring can contribute to relevant reductions of body weight of more than 5% [
Besides the reduction of body weight, there were further relevant improvements in cardiovascular risk factors such as BMI, LDL cholesterol, and waist circumference in the present study. In line with other lifestyle intervention studies with electronic devices (web-, app- or SMS-based lifestyle interventions), external motivation, electronically transmitted reminders, or personalized coaching contribute to meaningful improvements in cardiovascular risk factors [
The present study was well tolerated. The overall dropout rate after 12 weeks of intervention and during the 12-month follow-up was 6% and 14%, and no adverse events were reported. This low dropout rate was also shown in patients with heart diseases during their cardiac rehabilitation (<10%) which was characterized by using telemedical devices (pedometers) accompanied by telephone coaching [
In a cohort of obese employees with a mean age of 47 years, 470-600 EUR additional costs per year for obesity-related sick leave days had been estimated [
There are strengths and limitations in our study that should be mentioned. Overweight employees had been invited by their medical corporate department for participation in this study. Therefore, there could be a chance for a selection bias if only motivated employees agreed to participate. However, randomization into one of the three parallel groups should have abolished any potential effect, particularly, because baseline characteristics of the three groups were not different. On the other hand, a high motivation might have led to the low dropout rate of only 6% observed in our trial. According to the study size with 180 participants, the results of the present study might not be generalizable or transferable to other nonoccupational cohorts. In contrast to that, the study of Luley et al. demonstrated higher dropout rates of 9-12% during a 1-year lifestyle telemonitoring program for weight loss in obese patients with metabolic syndrome [
In sum, telemedical-based interventions are effective for long-term weight reductions in overweight employees. Especially in combination with continuous telephone coaching telemedical-based interventions demonstrated large effects on weight reduction and cardiovascular risk factors. These results underline the potential usefulness of telemedical monitoring and coaching for an occupational health care setting and could be an effective approach for preventive health care programs.
The data used to support the findings of this study are available from the corresponding author upon request.
Kerstin Kempf and Martin Röhling share the first authorship. HMM Holding AG had no influence on study design, data collection, data analysis, manuscript preparation, and/or publication decisions.
Kerstin Kempf and Stephan Martin received research support from HMM Holding AG. Monika Stichert, Martin Röhling, Gabriele Fischer, Elke Boschem, and Jürgen Könner declare that there are no conflicts of interest.
Kerstin Kempf and Stephan Martin contributed to conception and design of the study. Monika Stichert, Gabriele Fischer, Elke Boschem, and Jürgen Könner helped in data collection. Kerstin Kempf and Martin Röhling contributed to analysis and interpretation of data and drafting of the manuscript. Kerstin Kempf, Martin Röhling, Monika Stichert, Gabriele Fischer, Elke Boschem, Jürgen Könner, and Stephan Martin approved the final version of the manuscript. Kerstin Kempf and Martin Röhling equally contributed to manuscript.
The authors thank their coaches B. Prete and I. Grafflage for their excellent work and the HMM Holding AG for providing scales and 3D-step counters.
The supplementary material contains (i) a general description of each telemedical coaching interview (pages 1 and 2) and (ii) general information about the conditions of the telemedical coaching (page 3).