Mobile Health Systems for Bipolar Disorder

Mayora, Oscar; Frost, Mads; Arnrich, Bert; Gravenhorst, Franz; Grünerbl, Agnes; Muaremi, Amir; Osmani, Venet; Puiatti, Alessandro; Reichwaldt, Nina; Scharnweber, Corinna; Tröster, Gerhard

doi:10.4018/978-1-4666-8756-1.ch070

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…With portability and small size, use of smartphone app may indeed generate new security and privacy issues such as leakage and tampering of data transmitted over the wireless network [ 48 ], or stolen or lost device. Therefore, there is a need for all the sensor readings to be anonymized before analyzing them to guarantee the privacy of the participants [ 49 ]. As our research model did not include privacy concern as a construct, a future research direction is to integrate the privacy concern into the theoretical model.…”

Section: Discussionmentioning

confidence: 99%

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Dou

Deng

et al. 2017

JMIR Mhealth Uhealth

195

157

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BackgroundChronic disease patients often face multiple challenges from difficult comorbidities. Smartphone health technology can be used to help them manage their conditions only if they accept and use the technology.ObjectiveThe aim of this study was to develop and test a theoretical model to predict and explain the factors influencing patients’ acceptance of smartphone health technology for chronic disease management.MethodsMultiple theories and factors that may influence patients’ acceptance of smartphone health technology have been reviewed. A hybrid theoretical model was built based on the technology acceptance model, dual-factor model, health belief model, and the factors identified from interviews that might influence patients’ acceptance of smartphone health technology for chronic disease management. Data were collected from patient questionnaire surveys and computer log records about 157 hypertensive patients’ actual use of a smartphone health app. The partial least square method was used to test the theoretical model.ResultsThe model accounted for .412 of the variance in patients’ intention to adopt the smartphone health technology. Intention to use accounted for .111 of the variance in actual use and had a significant weak relationship with the latter. Perceived ease of use was affected by patients’ smartphone usage experience, relationship with doctor, and self-efficacy. Although without a significant effect on intention to use, perceived ease of use had a significant positive influence on perceived usefulness. Relationship with doctor and perceived health threat had significant positive effects on perceived usefulness, countering the negative influence of resistance to change. Perceived usefulness, perceived health threat, and resistance to change significantly predicted patients’ intentions to use the technology. Age and gender had no significant influence on patients’ acceptance of smartphone technology. The study also confirmed the positive relationship between intention to use and actual use of smartphone health apps for chronic disease management.ConclusionsThis study developed a theoretical model to predict patients’ acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients’ awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community.

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Section: Discussionmentioning

confidence: 99%

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Dou

Deng

et al. 2017

JMIR Mhealth Uhealth

195

157

View full text Add to dashboard Cite

show abstract

Objective assessment of depressive symptoms with machine learning and wearable sensors data

Ghandeharioun

Fedor

Sangermano³

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

106

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Mobile Health Systems and Electronic Health Record

Kasemsap

2018

Next-Generation Mobile and Pervasive Healthcare Solutions

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This chapter reveals the overview of mobile health systems; the adoption of mobile health systems; mobile health systems and patient monitoring; the overview of mobile health technology; the advanced issues of Electronic Health Record (EHR); and the challenges of EHR in global health care. Mobile health helps deliver the health care services with quality care, improved workflow, and increased patient interaction while minimizing complexity and cost to achieve the desired goals in health care settings. EHR systems are the real-time and patient-centered records that make information available instantly and securely to authorized users. The chapter argues that applying mobile health systems and EHR has the potential to improve health care efficiency and gain sustainable competitive advantage in global health care.

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Mobile Health Systems for Bipolar Disorder

Cited by 4 publications

References 8 publications

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Objective assessment of depressive symptoms with machine learning and wearable sensors data

Mobile Health Systems and Electronic Health Record

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