Smartphone Ownership and Interest in Mobile Health Technologies for Self-care Among Patients With Chronic Heart Failure: Cross-sectional Survey Study

BACKGROUND: Heart failure (HF) is a highly prevalent chronic condition that places a substantial burden on patients, families, and health care systems worldwide. Recent advances in mobile health (mHealth) technologies offer great opportunities for supporting many aspects of HF self-care. There is a need to better understand patients\u27 adoption of and interest in using mHealth for self-monitoring and management of HF symptoms. OBJECTIVE: The purpose of this study is to assess smartphone ownership and patient attitudes toward using mHealth technologies for HF self-care in a predominantly minority population in an urban clinical setting. METHODS: We conducted a cross-sectional survey of adult outpatients (aged \u3e /=18 years) at an academic outpatient HF clinic in the Midwest. The survey comprised 34 questions assessing patient demographics, ownership of smartphones and other mHealth devices, frequently used smartphone features, use of mHealth apps, and interest in using mHealth technologies for vital sign and HF symptom self-monitoring and management. RESULTS: A total of 144 patients were approached, of which 100 (69.4%) participated in the study (63/100, 63% women). The participants had a mean age of 61.3 (SD 12.25) years and were predominantly Black or African American (61/100, 61%) and Hispanic or Latino (18/100, 18%). Almost all participants (93/100, 93%) owned a cell phone. The share of patients who owned a smartphone was 68% (68/100). Racial and ethnic minorities that identified as Black or African American or Hispanic or Latino reported higher smartphone ownership rates compared with White patients with HF (45/61, 74% Black or African American and 11/18, 61% Hispanic or Latino vs 9/17, 53% White). There was a moderate and statistically significant association between smartphone ownership and age (Cramer V [PhiC]=0.35; P \u3c .001), education (PhiC=0.29; P=.001), and employment status (PhiC=0.3; P=.01). The most common smartphone features used by the participants were SMS text messaging (51/68, 75%), internet browsing (43/68, 63%), and mobile apps (41/68, 60%). The use of mHealth apps and wearable activity trackers (eg, Fitbits) for self-monitoring of HF-related parameters was low (15/68, 22% and 15/100, 15%, respectively). The most popular HF-related self-care measures participants would like to monitor using mHealth technologies were physical activity (46/68, 68%), blood pressure (44/68, 65%), and medication use (40/68, 59%). CONCLUSIONS: Most patients with HF have smartphones and are interested in using commercial mHealth apps and connected health devices to self-monitor their condition. Thus, there is a great opportunity to capitalize on the high smartphone ownership among racial and ethnic minority patients to increase reach and enhance HF self-management through mHealth interventions

Mobile health technology to promote physical activity in a workplace setting: A mixed methods pilot study in the police force

As work roles become more sedentary, new interventions to promote physical activity (PA) and reduce sedentary behaviour (SB) are required. Mobile health (mHealth) technology (including wearable activity monitors and smartphone apps) is increasingly popular, low cost and accessible. The project consisted of a systematic review of the existing evidence for mHealth interventions for PA and SB in workplace settings, followed by a real-world pre-post pilot study of a specific mHealth intervention in the police force (the Physical Activity Wearables in the Police Force, or PAW-Force study). The review findings indicated that mHealth is a potentially effective, feasible and acceptable tool for promoting PA in a workplace context, at least in the short term. The longer-term impact and acceptability, and the impact on SB and wider outcomes (such as health, wellbeing and productivity) were less clear. The methodological quality of many existing studies was low and there were few mixed methods and qualitative studies. The study aims were to address the evidence gaps identified in the review, including the use of mixed methods, a detailed exploration of feasibility, acceptability and engagement in both the short- and longer-term, in addition to the impact on PA, sedentary time, health and wellbeing, perceived stress, perceived productivity and sickness absence. This was the first known study of an mHealth intervention for PA and SB in the police force. Police officers and staff (n = 180) within the Devon and Cornwall and Dorset Police received a 12-week intervention (a Fitbit® activity monitor and ‘Bupa Boost’ smartphone app) with 8 months follow-up. The results suggested a potential short- and long-term impact of the intervention on PA, particularly for less active officers and staff. Although the intervention was perceived as acceptable and feasible at various organisational levels, the findings highlighted the importance of contextual factors and opportunity, particularly for reducing SB. As in previous studies in other workplace settings, the impact on health, wellbeing, stress, productivity and sickness absence was unclear. The fields of workplace wellness, mHealth and behaviour change are brought together in this PhD. The findings will inform future interventions in addition to policy and practice in the police force

Psychiatr Q

ObjectiveEffective and scalable lifestyle interventions are needed to address high rates of obesity in people with serious mental illness (SMI). This pilot study evaluated the feasibility of a behavioral weight loss intervention enhanced with peer support and mobile health (mHealth) technology for obese individuals with SMI.MethodsThe Diabetes Prevention Program Group Lifestyle Balance intervention enhanced with peer support and mHealth technology was implemented in a public mental health setting. Thirteen obese individuals with SMI participated in a pre-post pilot study of the 24-week intervention. Feasibility was assessed by program attendance, and participant satisfaction and suggestions for improving the model. Descriptive changes in weight and fitness were also explored.ResultsOverall attendance amounted to approximately half (56%) of weekly sessions. At 6-month follow-up, 45% of participants had lost weight, and 45% showed improved fitness by increasing their walking distance. Participants suggested a number of modifications to increase the relevance of the intervention for people with SMI, including less didactic instruction and more active learning, a simplified dietary component, more in depth technology training, and greater attention to mental health.ConclusionsThe principles of standard behavioral weight loss treatment provide a useful starting point for promoting weight loss in people with SMI. However, adaptions to standard weight loss curricula are needed to enhance engagement, participation, and outcomes to respond to the unique challenges of individuals with SMI.K12 HS021695/HS/AHRQ HHS/United StatesR01 MH089811/MH/NIMH NIH HHS/United StatesU48 DP005018/DP/NCCDPHP CDC HHS/United StatesU48DP005018/ACL HHS/United States2017-09-01T00:00:00Z26462674PMC4929042vault:1799

Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables

Wearable fitness trackers have gained a new level of popularity due to their ambient data gathering and analysis. This has signalled a trend toward self-efficacy and increased motivation among users of these devices. For consumers looking to improve their health, fitness trackers offer a way to more readily gain motivation via the personal data-based insights the devices offer. However, the user experience (UX) that accompanies wearables is critical to helping users interpret, understand, gain motivation and act on their data. Despite this, there is little evidence as to specific aspects of fitness tracker user engagement and long-term motivation. We report on a 4-week situated diary study and Healthcare Technology Self-efficacy (HTSE) questionnaire assessment of 34 users of two popular American fitness trackers: JawBone and FitBit. The study results illustrate design implications and requirements for fitness trackers and other self-efficacy mobile healthcare applications.We would like to thank all users who participated in this research as well as Experience Dynamics, Inc. for providing the necessary resources and coordinating the user diary study. This study has been supported by financial aid from the Spanish Ministry of Economy and Competitiveness under the project ECO2012-36160; Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and the Fondo Europeo de Desarollo Regional (FEDER) under the project ECO2015-67296-R and, Communidad de Madrid and Fondo Social Europeo under the project INNCOMCON-CM S2015/HUM-3417

Health Wearable Tools and Health Promotion

The application of wearable technology for health purposes is a multidisciplinary research topic. To summarize key contributions and simultaneously identify outstanding gaps in research, the input-mechanism-output (I-M-O) framework was applied to synthesize findings from 275 relevant papers in the period 2010–2021. Eighteen distinct cross-disciplinary themes were identified and organized under the I-M-O framework. Studies that covered input factors have largely been technocentric, exploring the design of various health wearables, with less emphasis on usability. While studies on user acceptance and engagement are increasing, there remains room for growth in user- centric aspects such as engagement. While measurement of physiological health indictors has grown more sophisticated due to sensitivity of sensors and the advancements in predictive algorithms, a rapidly growing area of research is that of measuring and tracking mental states and emotional health.Relatively few studies explore theoretically backed explanations of the role of health wearables, with technocentric theories predicting adoption favored. These mainly focused on mechanisms of adoption, while postadoption use and health behavior change were less explored. As a consequence, compared to adoption mechanisms, there is an opportunity to increase our understanding of the continued use of wearables and their effects on sustained health behavior change. While a range of incentives such as social, feedback, financial, and gamification are being tested, it is worth noting that negative attitudes, such as privacy concerns, are being paid much more attention as well. Output factors were studied in both individual and organizational settings, with the former receiving considerably more attention than the latter. The progress of research on health wearables was discussed from an interdisciplinary angle, and the role of social scientists was highlighted for the advancement of research on wearable health

Health habits and wearable activity tracker devices: analytical cross-sectional study

Wearable activity trackers are electronic devices that facilitate self-monitoring of information related to health. The purpose of this study was to examine the use of tracker devices to record daily activity (calories) and its associations with gender, generation, BMI, and physical activity behavior of United States of America resident adults; a cross-sectional study in 892 subjects recruited to participate in an anonymous online survey was performed. Being female increased the odds of using a tracker device by 2.3 times. Having low cardiovascular disease mortality risk related to time spent sitting increased the odds for using a tracker device by 2.7 times, and having medium risk 1.9 times, with respect to having high risk. For every 1-point increase in BMI, the odds for using a tracker device increased by 5.2%. Conclusions: Subjects who had ever used any tracker device had a higher BMI. The use of tracker devices was related to lower cardiovascular disease mortality risk related to sitting time. The amount of physical activity and the time spent walking were not associated with the usage of tracker devices. It is possible that the user of tracker devices should be supported by professionals to implement deep change in health habits

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors “in the wild” 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article

A comparison of video conferencing and in-person health coaching approaches in combination with mHealth devices on weight loss, physical activity, and glycemic control

Background: Compare health coaching efficacy on weight loss, physical activity, and glycemic control between individualized video conferencing (VC), in-person (IP) and control (CG) groups of adults with high BMI. Methods: Thirty adults (BMI≥30 kg/m2) were randomly assigned to create three groups of 10 members each. Participants received a wireless accelerometer watch and weight scale to sync with their personal smartphones and downloaded apps. Participants assigned to VC and IP received weekly health coaching individualized based on data uploaded over the 12-wk intervention. Steps/day and weight loss were analyzed via analyses of covariance (ANCOVA). Between-group ANOVAs analyzed post-intervention changes in weight (kg), glucose, HbA1c, and HOMA-IR. Results: Weight loss (8.23±4.5kg; 7.7%) was greater (pp≤.05); VC consistently had the higher step/day averages. No between-group differences were found for any glycemic control markers. Conclusion: Our innovative, multidisciplinary, telemedicine health coaching delivered through video conferencing led to favorable changes in weight loss, physical activity, and HOMA-IR that surpassed changes when health coaching was delivered in person or was absent. Future studies using video conferencing to investigate health coaching delivered in group and individualized formats and for other population subgroups are needed as are studies investigating the impact of weight loss on other health outcomes (e.g. lipid profile, glycemic control, and inflammatory markers)