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      The Effects of Self-Monitoring Using a Smartwatch and Smartphone App on Stress Awareness, Self-Efficacy, and Well-Being–Related Outcomes in Police Officers: Longitudinal Mixed Design Study

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          Abstract

          Background

          Wearable sensor technologies, often referred to as “wearables,” have seen a rapid rise in consumer interest in recent years. Initially often seen as “activity trackers,” wearables have gradually expanded to also estimate sleep, stress, and physiological recovery. In occupational settings, there is a growing interest in applying this technology to promote health and well-being, especially in professions with highly demanding working conditions such as first responders. However, it is not clear to what extent self-monitoring with wearables can positively influence stress- and well-being–related outcomes in real-life conditions and how wearable-based interventions should be designed for high-risk professionals.

          Objective

          The aim of this study was to investigate (1) whether offering a 5-week wearable-based intervention improves stress- and well-being–related outcomes in police officers and (2) whether extending a basic “off-the-shelf” wearable-based intervention with ecological momentary assessment (EMA) questionnaires, weekly personalized feedback reports, and peer support groups improves its effectiveness.

          Methods

          A total of 95 police officers from 5 offices participated in the study. The data of 79 participants were included for analysis. During the first 5 weeks, participants used no self-monitoring technology (control period). During the following 5 weeks (intervention period), 41 participants used a Garmin Forerunner 255 smartwatch with a custom-built app (comparable to that of the consumer-available wearable), whereas the other 38 participants used the same system, but complemented by daily EMA questionnaires, weekly personalized feedback reports, and access to peer support groups. At baseline (T0) and after the control (T1) and intervention (T2) periods, questionnaires were administered to measure 15 outcomes relating to stress awareness, stress management self-efficacy, and outcomes related to stress and general well-being. Linear mixed models that accounted for repeated measures within subjects, the control and intervention periods, and between-group differences were used to address both research questions.

          Results

          The results of the first analysis showed that the intervention had a small (absolute Hedges g=0.25‐0.46) but consistent effect on 8 of 15 of the stress- and well-being–related outcomes in comparison to the control group. The second analysis provided mixed results; the extended intervention was more effective than the basic intervention at improving recovery after work but less effective at improving self-efficacy in behavior change and sleep issues, and similarly effective in the remaining 12 outcomes.

          Conclusions

          Offering a 5-week wearable-based intervention to police officers can positively contribute to optimizing their stress-related, self-efficacy, and well-being–related outcomes. Complementing the basic “off-the-shelf” wearable-based intervention with additional EMA questionnaires, weekly personalized feedback reports, and peer support groups did not appear to improve the effectiveness of the intervention. Future work is needed to investigate how different aspects of these interventions can be tailored to specific characteristics and needs of employees to optimize these effects.

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          Most cited references68

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Statistical Power Analysis for the Behavioral Sciences

            <i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>
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              A general and simple method for obtainingR2from generalized linear mixed-effects models

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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                mhealth
                13
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                2025
                28 January 2025
                : 13
                : e60708
                Affiliations
                [1 ]departmentDepartment of Learning and Workforce Development , The Netherlands Organisation for Applied Scientific Research , Soesterberg, Netherlands
                [2 ]departmentDepartment of Work Health Technology , The Netherlands Organisation for Applied Scientific Research , Leiden, Netherlands
                [3 ]departmentDepartment of Sustainable Productivity and Employability , The Netherlands Organisation for Applied Scientific Research , Leiden, Netherlands
                Author notes
                WimKamphuisPhD, Department of Learning and Workforce Development, The Netherlands Organisation for Applied Scientific Research, PO Box 23, Soesterberg, 3769 ZG, Netherlands, 0031 888661500; wim.kamphuis@ 123456tno.nl

                None declared.

                Author information
                http://orcid.org/0000-0003-1196-5062
                http://orcid.org/0000-0001-6897-8716
                http://orcid.org/0000-0002-2789-4820
                http://orcid.org/0009-0008-3121-071X
                http://orcid.org/0000-0002-8363-9277
                Article
                60708
                10.2196/60708
                11793834
                39881435
                349abfbb-2a11-4b39-a1ed-9230792cb6c8
                Copyright © Herman Jaap de Vries, Roos Delahaij, Marianne van Zwieten, Helen Verhoef, Wim Kamphuis. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org)

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 19 May 2024
                : 15 August 2024
                : 14 October 2024
                Categories
                mHealth for Wellness, Behavior Change and Prevention
                Wearable Devices and Sensors
                Mobile Health (mhealth)
                Fitness Trackers and Smart Pedometers/Accelerometers
                mHealth for Data Collection and Research
                Formative Evaluation of Digital Health Interventions
                Posttraumatic Stress Disorder (PTSD)
                Anxiety and Stress Disorders
                Ecological Momentary Assessment (EMA)
                Original Paper

                wearable electronic devices,ecological momentary assessment,psychological stress,psychological well-being,awareness,self-efficacy,occupational medicine,emergency responders,well-being,psychological,efficacy,stress,wearables,wearable device,smartwatch,smartphone app,app,sensor,sensor technology,police officers,questionnaire,stress awareness,stress management

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