The effect of active occupational stress management on psychosocial and physiological wellbeing

Background: The aim of the study was to address the working population with an occupational stress prevention program using mHealth solution and encourage healthy lifestyle choices. Methods: 16 participants were randomized from the corporate setting. A 24alife app with a good compliance program was selected. Test battery has been designed to test the physical readiness, psychological state of stress and to assess biological blood markers for stress. Participants were followed up after 30, 60 and 90 days, respectively, within the intervention period. Blood pressure and weight were tracked 3 times per month. At least once a week, but on average 6 times per month, participants also led the food diary. Univariate analysis compared the continuous variables by Student's t-test for the data that were normally distributed, or Wilcoxon rank sum test for abnormal distribution of variables. Results: Participants used the app with a compliance rate of 100%. The psychological evaluation revealed higher motivation for work, lower burnout scores and participants gave subjective responses of better general wellbeing. Some of the participants lost up to 4 kg of body mass. Physical readiness has significantly improved. Conclusions: Results of mHealth projects on corporate could include primary health care institutions and health ministry to extend the existing system to patients’ pockets where they can monitor their disease and increase the ability of self-care. Walking The a 1.600 (m) walk test in which participants are to walk the distance as as possible. Results of several studies support the use of the RFWT as a reliable and valid instrument for adults with intellectual disabilities (ID). The test-retest reliability of the RFWT (r 0.99) was established for 19 adult males with ID. 10 Each participant underwent an aerobic power testing at our first meeting.

kinesiology, psychology and nutrition. The automated program was individually adapted and designed to lower stress, increase productivity, decrease absenteeism and increase motivation on the workplace. The aim of this study was to offer and test the intervention program and behavioural changing pattern in workers, who experienced different levels of strain at workplace. Participants were tested for psychological and physiological parameters prior intervention, and were followed up after 30, 60 and 90 days, respectively, within the intervention period. With the preliminary analysis, we aim to test for the optimal time period of an intervention program and gather the data on the psychological and behavioural changes at each time point to reach a conclusion on the most appropriate time frame of the intervention.

Materials And Methods Subjects
We have randomly selected 16 participants from the corporate setting. The participants were given written informed consent form and research information sheet prior the inclusion into the study.
Sixteen participants, 8 males and 8 females, decided to undergo the testing, whereas one participant has been excluded from the study due to inability to finish the program with the final testing. The mean age of participants was 34.93 (SD = 10.10) years. Personal details such as marital status, children, or personal conflicts were not obtained. No investigator had access to the participants' health screen or medical records to ensure confidentiality. Under the principles of protection of human subjects, no information was gathered on individuals, who declined participation. The study procedure was conducted according to the Declaration of Helsinki and approved by the departmental committee. Volunteers gave written consent of participation.

Test battery
Test battery has been designed to test the physical readiness, psychological state of stress and to assess biological blood markers for stress. The participants underwent testing on the first day of the intervention and then 30, 60 and 90 days after starting the intervention.
Physical readiness evaluation. Participants' cardiovascular fitness levels were measured using the Rockport Fitness Walking Test. 10 Participant's level of flexibility has been measured with a standardized Sit and Reach Test. Level of muscular strength has been measured by the Sit up test.

Rockport test (Vo2max test)
Participants' cardiovascular fitness levels were measured using the Rockport Fitness Walking Test (RFWT). 10 The RFWT is a 1.600 meter (m) walk test in which participants are instructed to walk the distance as quickly as possible. Results of several studies support the use of the RFWT as a reliable and valid instrument for adults with intellectual disabilities (ID). The test-retest reliability of the RFWT (r = 0.99) was established for 19 adult males with ID. 10  Psychological evaluation. 24alife stress profile with the help of questionnaires: personal hardiness, somatic anxiety, cognitive anxiety, self-confidence, coping strategies, well-being, general (un)health, burn out, environment. The questionnaires we were using were as follows.
General Health Questionnaire (GHQ) is a 12-item version or GHQ-12, 11,12 a self-reported instrument of psychological components of health. The GHQ-12 focuses on breaks in normal function (rather than upon lifelong traits) and concerns itself with two major classes of phenomena: inability to continue to carry out one's normal ``healthy'' functions and the appearance of new phenomena of a distressing nature.
Modified Fatigue Impact Scale (MFIS) 13 is a well validated 21-item questionnaire assessing several aspects of fatigue and activity. The scale was developed for clinical population particularly for the patients with multiple sclerosis, however it has been used in a healthy population for research purposes. Higher scores indicate a higher degree of fatigue. The scale can be interpreted in subscales of physical fatigue, cognitive fatigue, psychosocial fatigue, and the overall score of the fatigue scale.
The MFIS contains 9 "physical" items, 10 "cognitive" items, and 2 "psychosocial" items. The maximum possible score is 84, with higher scores indicating a greater impact on quality of life. The original intention was to use the total score to reflect a global (unidimensional) score when reporting fatigue.
State-Trait Anxiety Inventory (STAI -X) measures anxiety as a stable personality trait, a persons' disposition to be nervous instead of the more prominent use of the term assessing an emotional state characterized by subjective feelings of tension, apprehension, nervousness and worry, and by activation or arousal of the autonomic nervous system. 14,15 Form X of the STAI contains 20 state anxiety items and 20 trait anxiety items. The state anxiety items are each rated on a 4-point intensity scale, from 1 for "not at all" to 4 for "very much so." The trait anxiety items are rated on a 4-point frequency scale (from "almost never" to "almost always"). Respondents are asked to indicate how they generally feel. Scoring is reversed for anxiety-absent items (e.g., "I feel calm"). STAI was developed as a unidimensional self-report measure. 10 items are positively worded, and 10 items are negatively worded. Score range is 20-80 and higher scores indicate greater levels of anxiety. Testretest reliability tends to be high for Trait and low for State, as expected. Test-retest for STAI Trait ranges from 0.73 to 0.86 over periods of 1 hour to 104 days. Alpha coefficients for both tests range from 0.83 to 0.95. STAI State and STAI Trait correlate between 0.59 and 0.75. The State Anxiety Scale is intended to measure transient levels of anxiety and, as such, is not expected to have high test-retest relationships. The Trait Anxiety Scale measure is positional anxiety and has been shown to be relatively stable over time.
Satisfaction with Life Scale (SWLS). 16 The satisfaction with life was obtained by assessing global cognitive judgment of participants' view of their life on a 5-item scale. There is a 7-point scale from "strongly disagree" to "strongly agree" and a score range is  Maslow Burnout Inventory (MBI). The MBI was created in 1996 by Maslach, Jackson and Leiter. 17 The MBI is the most well-known measure of teacher burnout and has been used in more than 90% of empirical studies on the subject. 18,19 The three main components of burnout measured by the 22 questions on the MBI include: emotional exhaustion, depersonalization and personal accomplishment.
Each of these three scores is measured using questions answered with a 7-point frequency scale and the answers range from 0 ("never") to 6 ("everyday"). "Depersonalization" occurs when a teacher isolates himself from others. This variable is measured with five items on the survey that ask for the frequency with which they experience negative feelings towards other teachers and administrators.
"Personal accomplishment" is the self-evaluation of the efficacy of the teacher's own work. Eight items on the survey test the teacher's feelings of personal accomplishment. "Emotional exhaustion" measures fatigue, frustration, and stress. Nine questions on the survey are used to create a score for this component. Since they are measured by frequency, the personal accomplishment scores were reverse-coded to match the consistency of the results. 20 The average of each of the twenty-two questions yields a burnout score for individual participants.
Biological stress marker evaluation. Biological stress markers have been repeatedly evaluated and participants have been tested for the blood pressure, resting heart rate and weight. Blood tests of biochemistry and hormonal levels have been performed at the beginning and at the end of evaluation. Blood samples (EDTA tubes, 3 mL) were collected during the morning hours; plasma and cell fractions were processed on the same day of collection. Plasma aliquots were stored at -80 °C until use.
Intervention Automated Software Program. The participants have been asked to actively participate and follow the automated software-based program (24alife app), which has been designed by the experts from the fields of psychology, medicine, sports science and nutrition to holistically approach forming healthy lifestyle habits. The intervention program was a mobile app guide with daily reminders and tasks, designed to guide individual through healthy lifestyle for 3 months. Based on user's initial state, which was specified testing battery, the algorithm behind the software dedicated the intensity of the

Statistical analysis
Statistical analysis was performed by SPSS 21 software (IBM, New York, USA). Univariate analysis compared the continuous variables by Student's t-test for the data that were normally distributed, or Wilcoxon rank sum test for abnormal distribution of variables. Statistical differences were considered to be significant at P value < 0.05.

Results
The results showed that participants well accepted and frequently used the 24alife app with good compliance to the program. The compliance rate was 100% excluding the subject, who could not perform the tests. Participants on average tracked their BP and weight 3 times per month as suggested. At least once a week, but on average 6 times per month, participants led the food diary.
They followed 90 minutes of guided sport exercises for aerobic and strength training per week and listened to 7 minutes of relaxation exercises per day (not shown).
The psychological evaluation revealed higher motivation for work, lower burnout scores and participants gave subjective response of better general wellbeing (not shown). They have found the experience to enhance their team-work and commitment to work activities. Additionally, they have spontaneously organized group activities in their free time without researchers' knowledge.
On average, the body measurements have not improved much, however, on the individual level, some of the participants lost up to 4 kg of body mass. There was a trend of a positive change by the end of the program (Table 1). Most changes were observed after 90 days. Physical readiness was statistically significantly improved according to the results of Rockport test times (Table 2). Most evident positive results were seen in the biological markers of stress (Table 3). A significant decrease in stress hormone cortisol, CRP and glucose level by the end of the program was observed (Table 3).

Discussion
Mobile applications have been developed to collect physiological, psychological and performance data, however the reliability and validity of such data are often unknown. 21 An evaluation of such apps is warranted and necessary, as according to review by Buechi et al. 22 there are more than 165.000 available applications to download and not all are showing reliable data process. Whilst mobile apps may have the potential to collect data, athletes and practitioners should take caution when implementing them into practice. 24alife not only handles the standard physiological and psychological tests, but also supports medical data and correlates with blood markers.
Until now, only three reviews have been published, which have focused on the efficacy, effectiveness and usability of mHealth apps, 23−25 and there are no randomized trials dealing with the scientific evaluation of mHealth apps. Research with diagnostic studies are missing since most apps target a diagnostic problem without the scientific basis. 22 Our study is a good example of how an mHealth solution can be integrated into the OHP and prevention programs. The results show significant changes in levels of stress hormones which could be due to increased level of physical activity and frequency of deep relaxation during a regular week of employees. The significant decrease of the glucose level could be explained by the lower level of stress along with dietary changes which participants adopted by following the advice and daily reminders on the mobile phone app. Another reason is also, that endurance training recognizes the input from the brain, including an ability to cope with various non-pleasurable perceptions during exercise, such as pain and temperature.
Exercise training can reduce perceptions of pain and temperature over time, 26 so the participants could feel more relaxed in every-day situations.
Similar was observed in the study by Stuckey et al. 27 where the authors investigated the effects of an exercise prescription supported by mHealth app compared to an exercise prescription alone to improve metabolic syndrome and cardiometabolic risk factors. The primary outcome was that systolic blood pressure was greatly reduced in the mHealth-supported intervention group at 12-weeks followup and this improvement was better maintained for the whole trial year. In general, exercise has positive health benefits. The effects of exercise on individual metabolic syndrome risk factors have been previously extensively reviewed. 28−30 Training interventions were 8 to 52 weeks, frequency of 2 to 5 sessions per week for 40-60 minutes per session at moderate to high intensity. Endurance exercise training reduced waist circumference, lowered systolic blood pressure and diastolic blood pressure and increased high density lipoprotein cholesterol. 31 Additionally, lifestyle interventions have also shown to improve autonomic 32 − 35 and vascular function as well. 36,37 Importantly, improvements in heart rate variability with lifestyle interventions were associated with lower risk of diabetes, independent of weight loss and physical activity. 38 Use of the mHealth tools for fitness assessment and exercise prescription has been observed to improve the general health and feeling of subjects. 39 Increased cardiorespiratory fitness was shown to reduce mortality due to cardiovascular events in men. 40 Intensive lifestyle intervention programs reduced the risk of developing type 2 diabetes mellitus in patients with prediabetes and improvements were maintained over a long-term follow-up. 41 All these findings and the results of our preliminary study highlight the importance of post-program support for long-term maintenance. Using mHealth apps improves participants/users' health and benefits in reducing work strain.
Implementation of such activities are important to translate research protocols into useable programs including the construction of a national network of partners with interest in chronic disease management.
Our study only shows preliminary results and is subjected to many limitations preventing us to draw conclusions on the efficacy of the automated program. However, it shows that on individual level, there were positive changes towards healthier lifestyle habits, and we found lower levels of perceived and biological stress among participants. We need the follow-up study, which will be able to reveal the factors that are necessary for the e-solution to be successful in health changes. Our study had strengths and limitations. This study assessing the performance of diagnostic health app using smartphone sensors showed that scientific evidence is necessary. Our study design was good, and the major strength was the detailed preparatory work which was undertaken to develop the application and specifically adapt it into corporate. Another strength is the subsequent evaluation and usability indicators of the monitoring system. This is one of the few apps currently available on app stores that include assessed parameters of diagnostic accuracy. A limitation is that the pilot study period was relatively short, and it is therefore not possible to distinguish long-term results. A larger study sample including athletes, semi-professional athletes, and more employees may also have provided further insights into the feasibility and usability of this relatively novel eHealth application.
Participants volunteered to be enrolled in the study. Thus, the study population consisted of highly motivated individuals, who were interested in improvements of their lifestyle. Therefore, the findings may not be generalizable to a general clinic population, as many at-risk patients are not motivated to change their behaviours.

Conclusion
In health issues, where changing lifestyle is one of the crucial factors for slowing the progress of chronic disease such as stress, burnout or diabetes, patients could benefit from holistic personal monitor such as 24alife. We showed that preliminary results of mHealth projects on corporate could include primary health care institutions and health ministry to extend the existing system to patients' pockets where they can monitor their disease and increase the ability of self-care. Based on the results from a healthy population we could create a tool that will ease the patients' transition from the first diagnose to self-care, and most importantly create a solution where the management of chronic illness will be more successful, patients will be able to change their lifestyle and, by the help of support and reminders, form healthier habits with long-term positive consequences to their health.

Declarations
Ethical approval and consent to participate Institutional Ethical Committee from Medical faculty of Ljubljana approved the study

Consent for publication
The study does not contain data from any individual person, but volunteers gave written consent of participation.

Availability of data and materials
The data that support the findings of this study are available from the company RC IKTS Žalec but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the company RC IKTS Žalec.

Competing interests
The authors declare that they have no competing interests

Funding
The execution of the study was supported by the company RC IKTS Žalec from Slovenia, which developed a 24alife mobile application