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Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a sister journal of JMIR, the leading eHealth journal. JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, Scopus, and Science Citation Index Expanded (SCIE), and in June 2018 received an Impact Factor of 4.541, which ranks the journal #2 (behind JMIR) out of 25 journals in the medical informatics category indexed by the Science Citation Index Expanded (SCIE) by Thomson Reuters/Clarivate.
The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.
JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.
JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics.
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Background: Impulsive processes driving eating behaviour can often undermine peoples’ attempts to lose weight and maintain weight loss. Objective: To develop an impulse management intervention to su...
Background: Impulsive processes driving eating behaviour can often undermine peoples’ attempts to lose weight and maintain weight loss. Objective: To develop an impulse management intervention to support weight loss in adults. Methods: Intervention Mapping (IM) was used to systematically develop the “ImpulsePal” intervention. The development involved: 1) a needs assessment including a qualitative study, service user workshops, a systematic review of impulse modification techniques, and consultations with intervention design and delivery experts; 2) specification of performance objectives, determinants, and change objectives; 3) selection of intervention strategies (mapping of taxonomy-related change techniques to the determinants of change); 4) creation of programme materials; 5) specification of adoption and implementation plans; 6) devising an evaluation plan. Results: Application of the IM Protocol resulted in a smartphone app-based intervention aimed at reducing unhealthy snacking, overeating, and alcoholic and sugary drink consumption. The app includes inhibition training, mindfulness techniques, implementation intentions (if-then planning), visuospatial loading, use of physical activity as a craving-management technique, and context-specific reminders. An “Emergency Button” was also included to provide access to in-the-moment support when temptation is strong. Conclusions: ImpulsePal is a novel, theory- and evidence-informed, person-centred app to improve impulse management and promote healthier eating. Intervention Mapping ensured that all app components are practical operationalisations of change techniques that target our specific change objectives and their associated theoretical determinants. Using this approach enhances transparency, provides a clear framework for analysis and increases replicability as well as the potential of the intervention to accomplish the desired outcome of supporting weight loss.
Background: The increased wearability of technology has enabled individuals to access and interact with technology in such a way that it has become more and more an integral part of one’s body, mind...
Background: The increased wearability of technology has enabled individuals to access and interact with technology in such a way that it has become more and more an integral part of one’s body, mind, and sense of self. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities to quantify wearable technology embodiment are of particular value to the mHealth field. For example, to predict the effectiveness of mHealth interventions, knowing the extent to which people embody the technology might be crucial. To facilitate examining wearable technology embodiment we developed a measurement scale for this construct. Objective: The wearable technology embodiment scale is a 3 dimensional scale including 9 measurement items. The items are distributed evenly between the 3 dimensions which include: body extension, cognitive extension, and self-extension. The objective of the study was to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well known constructs related to technology adoption. Methods: Data were collected through a vignette-based survey (n=182). Each respondent was given three different vignettes, describing a hypothetical situation using a different type of mobile or wearable technology with the purpose of tracking daily activities: a smart phone, a smart wristband, and a smart watch. The scale dimensions and item reliability was tested for validity and goodness of fit. Results: Convergent validity of the three dimensions and their reliability was confirmed via the CFA factor loadings45 (> 0.70), AVE values40 (> 0.50) and minimum item to total correlations50 (> 0.40) which exceeded established threshold value. The reliability of the dimensions was also confirmed as cronbach’s alpha and composite reliability exceeded 0.70. Good fit was found within three dimensions as inter-correlated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (i.e.: trust, perceived usefulness, involvement, attitude, and continuous intention). Conclusions: The wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual’s body, cognition, and self, as well as predict certain aspects of technology adoption. This three dimensional tool can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as: fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users personal identity. Further research is recommended to apply this tool to multiple scenarios and technologies, and more diverse user groups.
Background: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-bas...
Background: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or hand-crafted machine learning techniques limited in terms of diagnostic accuracy and reliability. Objective: We developed deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion. Methods: We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). ECG and pulse oximetry data over a 15-minute period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the two DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (root-mean square of successive difference of RR intervals + Shannon entropy, support vector machine with autocorrelation and ensemble by combining two prior methods) using a five-fold cross-validation process. Results: Among the 14,298 training samples containing PPG data, 7,157 samples were obtained during the post-DCC period. The PAC indicator estimated 2,132 out of 7,157 post-DCC samples (29.79%) had PACs. The diagnostic accuracy of AF vs. SR was 99.3% vs. 95.9% in 1D-CNN and 98.3% vs. 96.0% in RNN methods. The area under receiver operating characteristic curves of the two DL classifiers was 0.998 (95% confidence interval (CI) 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors (P < .001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers could still correctly diagnose AF even when the PAC burden was >20% (91.1% and 91.5% for 1D-CNN and RNN, respectively). The average CLs for true vs. false classification were 98.6% vs. 80.5 % for 1D-CNN and 98.3% vs. 82.4% for RNN (P < .001 for all cases). Conclusions: New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL. Wearable devices sensing PPG signals and DL classifiers should be validated as tools to screen for AF.
Background: mHealth is a broad term for the use of mobile communication devices for healthcare services delivery. The use of mobile devices by health care professionals (HCPs) has transformed many asp...
Background: mHealth is a broad term for the use of mobile communication devices for healthcare services delivery. The use of mobile devices by health care professionals (HCPs) has transformed many aspects of clinical training and practice. However, there are still gaps in knowledge concerning patient perception of the use of mHealth technologies by HCP during secondary care consultations. Objective: To explore the impact on patient experience and implications for consultation outcomes and treatment adherence. Introduction of new technological application into interactions that have very set expectations and roles and possibility for attendant disruption of patient expectations. Methods: This paper explores, via in-depth interviews, patient opinions regarding the usage of mHealth applications by health care professionals (HCPs) during consultations, identifying the paradoxes and coping behaviors to deal with those paradoxes. This qualitative study recruited ten respondents using purposive sampling and snowballing techniques through in-depth interviews. Results: The results comprise paradoxes and coping behaviors. They showed that convenience, time savings, accuracy of diagnosis and reduction of errors are the important elements for using mHealth for both HCP and patient. In addition, respondents perceived that mobile health apps facilitate HCP engagement of patients and assist explanations and better patient understanding. Interaction and the quality of the interaction were acknowledged as significant in HCP-patient communication and patient compliance with treatment. Conclusions: To sum, many patients were responsive to the idea of mHealth, both by the doctor and themselves, but wanted to have regulation of use of apps, better involvement and explanations and not have the doctor lose focus on the patient, that is, the feeling of personalized treatment. They also were worried that the HCP might seem to ignore the patient or withdraw from the type of interaction that makes the consultation ‘human.’
Background: Mobile applications have been considered to provide active and continuous support for smoking cessation. However, it is yet to be known whether a smoking cessation smartphone application i...
Background: Mobile applications have been considered to provide active and continuous support for smoking cessation. However, it is yet to be known whether a smoking cessation smartphone application improves long-term abstinence rates in nicotine-dependent patients. Objective: We aim to evaluate the long-term abstinence effect of a novel smartphone application, CureApp Smoking Cessation (CASC), in patients with nicotine dependence. Methods: This study was a prospective, interventional, multicenter, single-arm study. We provided the CASC application to all the participants, who used it daily for 24 weeks. The CASC application includes features to maximize the therapeutic effect of pharmacological therapies and counseling at outpatient clinics for smoking cessation. The primary endpoint was a continuous abstinence rate (CAR) from weeks 9 to 24, while secondary endpoints were CARs from weeks 9 to 12 and 9 to 52. Results: Of the 56 adult smokers recruited, one did not download the application; therefore, 55 participants constituted the full analysis sample. The CAR from weeks 9 to 24 was 63.6% (35/55; 95% confidence interval [CI]: 51.3% – 76.0%), while the CARs from weeks 9 to 12 and 9 to 52 were 76.4% (42/55; 95% CI: 65.2% – 87.5%) and 58.2% (32/55; 95% CI: 45.6% – 70.8%), respectively. These CARs were better than the results of the national survey on outpatient clinics with regard to smoking cessation under the National Health Insurance Program and that of the varenicline phase Ⅲ trial in Japan and the United States. There was only one participant who dropped out during the 12 weeks of the treatment period. This treatment decreased the scores related to withdrawal and craving symptoms. Conclusions: The addition of CASC to usual smoking cessation therapies resulted in high CARs, high patient retention rates, and improvement of cessation-related symptoms. The smartphone application CASC is a feasible and useful tool to help long-term continuous abstinence that can be combined with a standard smoking cessation treatment program. Clinical Trial: University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN ID: UMIN000020123).
Background: Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective t...
Background: Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective tool to engage patients more actively in the management of their chronic diseases. However, no PHRs currently integrate PGHD directly from Samsung S-Health and Apple Health. Objective: The purposes of this study were (1) to demonstrate the development of an electronic medical record (EMR)-tethered PHR system (Health4U) that integrates lifelog data from Samsung S-Health and Apple Health and (2) to explore the factors associated with the use rate of the functions. Methods: To upgrade conventional EMR-tethered PHRs, a task-force team (TFT) defined the functions necessary for users. After implementing a new system, we enrolled adults aged 19 years and older with prior experience accessing Health4U in the 7-month period after November 2017, when the service was upgraded. Results: Among the 17,624 users, 215 users integrated daily steps data, 175 weight data, 51 blood sugar data, and 90 blood pressure data. Among all participants, 61.95% had one or more chronic diseases. For the integration of daily steps data, 48.3% of patients used Apple Health, 43.3% used S-Health, and 8.4% entered data manually. To retrieve medical documentation, 324 users downloaded PDF files, and 31 users integrated their medical records into Samsung S-Health via the C-CDA download function. We found a consistent increase in the odds ratios for PDF downloads among patients with a higher number of chronic diseases. The age groups of 60 years and older and 80 years and older tended to use the download function less frequently. Conclusions: This is the first study examining the factors related to the integration of lifelog data from Samsung S-Health and Apple Health into EMR-tethered PHRs and the factors related to the retrieval of medical documents from PHRs. Furthermore, findings on lifelog data integration can be used to design PHRs as a platform to integrate lifelog data in the future.