Adoption of health information technologies: the case of a wireless monitor for diabetes and obesity patients

Slip Buddy App for Weight Management: Randomized Feasibility Trial of a Dietary Lapse Tracking App

BACKGROUND

Although calorie tracking is one of the strongest predictors of weight loss in behavioral weight loss interventions, low rates of adherence are common.

OBJECTIVE

This study aims to examine the feasibility and acceptability of using the Slip Buddy app during a 12-week web-based weight loss program.

METHODS

We conducted a randomized pilot trial to evaluate the feasibility and acceptability of using the Slip Buddy app compared with a popular commercial calorie tracking app during a counselor-led, web-based behavioral weight loss intervention. Adults who were overweight or obese were recruited on the web and randomized into a 12-week web-based weight loss intervention that included either the Slip Buddy app or a commercial calorie tracking app. Feasibility outcomes included retention, app use, usability, slips reported, and contextual factors reported at slips. Acceptability outcomes included ratings of how helpful, tedious, taxing, time consuming, and burdensome using the assigned app was. We described weight change from baseline to 12 weeks in both groups as an exploratory outcome. Participants using the Slip Buddy app provided feedback on how to improve it during the postintervention focus groups.

RESULTS

A total of 75% (48/64) of the participants were female and, on average, 39.8 (SD 11.0) years old with a mean BMI of 34.2 (SD 4.9) kg/m2. Retention was high in both conditions, with 97% (31/32) retained in the Slip Buddy condition and 94% (30/32) retained in the calorie tracking condition. On average, participants used the Slip Buddy app on 53.8% (SD 31.3%) of days, which was not significantly different from those using the calorie tracking app (mean 57.5%, SD 28.4% of days), and participants who recorded slips (30/32, 94%) logged on average 17.9 (SD 14.4) slips in 12 weeks. The most common slips occurred during snack times (220/538, 40.9%). Slips most often occurred at home (297/538, 55.2%), while working (153/538, 28.4%), while socializing (130/538, 24.2%), or during screen time (123/538, 22.9%). The conditions did not differ in participants' ratings of how their assigned app was tedious, taxing, or time consuming (all values of P>.05), but the calorie tracking condition gave their app higher helpfulness and usability ratings (all values of P<.05). Technical issues were the most common type of negative feedback, whereas simplicity was the most common type of positive feedback. Weight losses of ≥5% of baseline weight were achieved by 31% (10/32) of Slip Buddy participants and 34% (11/32) of calorie tracking participants.

CONCLUSIONS

Self-monitoring of dietary lapses and the contextual factors associated with them may be an alternative for people who do not prefer calorie tracking. Future research should examine patient characteristics associated with adherence to different forms of dietary self-monitoring.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02615171; https://clinicaltrials.gov/ct2/show/NCT02615171.

Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study

Background

Constantly increasing health care costs have led countries and health care providers to the point where health care systems must be reinvented. Consequently, electronic health (eHealth) has recently received a great deal of attention in social sciences in the domain of Internet studies. However, only a fraction of these studies focuses on the acceptability of eHealth, making consumers’ subjective evaluation an understudied field. This study will address this gap by focusing on the acceptance of MyData-based preventive eHealth services from the consumer point of view. We are adopting the term "MyData", which according to a White Paper of the Finnish Ministry of Transport and Communication refers to "1) a new approach, a paradigm shift in personal data management and processing that seeks to transform the current organization centric system to a human centric system, 2) to personal data as a resource that the individual can access and control."

Objective

The aim of this study was to investigate what factors influence consumers’ intentions to use a MyData-based preventive eHealth service before use.

Methods

We applied a new adoption model combining Venkatesh’s unified theory of acceptance and use of technology 2 (UTAUT2) in a consumer context and three constructs from health behavior theories, namely threat appraisals, self-efficacy, and perceived barriers. To test the research model, we applied structural equation modeling (SEM) with Mplus software, version 7.4. A Web-based survey was administered. We collected 855 responses.

Results

We first applied traditional SEM for the research model, which was not statistically significant. We then tested for possible heterogeneity in the data by running a mixture analysis. We found that heterogeneity was not the cause for the poor performance of the research model. Thus, we moved on to model-generating SEM and ended up with a statistically significant empirical model (root mean square error of approximation [RMSEA] 0.051, Tucker-Lewis index [TLI] 0.906, comparative fit index [CFI] 0.915, and standardized root mean square residual 0.062). According to our empirical model, the statistically significant drivers for behavioral intention were effort expectancy (beta=.191, P<.001), self-efficacy (beta=.449, P<.001), threat appraisals (beta=.416, P<.001), and perceived barriers (beta=−.212, P=.009).

Conclusions

Our research highlighted the importance of health-related factors when it comes to eHealth technology adoption in the consumer context. Emphasis should especially be placed on efforts to increase consumers’ self-efficacy in eHealth technology use and in supporting healthy behavior.

A meta-analysis of e-health applications acceptance

Purpose

The increasing importance of electronic health (e-health) has raised the significance of exploring the factors influencing the users’ acceptance of e-health applications. There has been an extensive usage of the technology acceptance model (TAM) in e-health applications acceptance research. However, not all TAM relationships are borne out in all the studies. There is a variation in predicted effects in several studies with different user type and application type. The purpose of this paper is to bridge a research gap by providing a holistic view of the e-health applications acceptance research by integrating the findings of existing relevant literature.

Design/methodology/approach

A statistical meta-analysis of the effect size of causal relationships between common TAM constructs was conducted on 111 peer-reviewed academic studies published in various journals.

Findings

The results confirm the validity and robustness of the TAM in e-health applications acceptance research. Further, a moderator analysis based on user type and e-health application type demonstrated that the effect size of causal relationships between TAM constructs majorly depends on the user type, but not on e-health application type.

Research limitations/implications

This research provides a ready reference of the existing studies on e-health applications acceptance to the researchers. Further, if researchers or practitioners want to learn more about the particular user or application type, they may find the results valuable.

Practical implications

This research suggests that the general public can be used as the surrogates for patients in e-health applications acceptance research. The implementation strategy found successful for medical staff may not work for general public and patients. This research also suggests ways to enhance acceptance of e-health applications among different user groups.

Originality/value

The research is original and is based on the existing literature and its interpretation.

Adoption of Mobile Devices in the Australian Healthcare

In recent years, the potential use of mobile devices has significantly positively impacted healthcare globally. Despite global claims of widespread use of mobile devices in healthcare, its adoption in the Australian healthcare context is slow. Limited research is available on slow adoption of mobile devices in the Australian healthcare context. Therefore, this chapter is designed to investigate the factors influencing adoption of mobile devices from healthcare professionals' perspective and develop a health specific conceptual framework. The influential factors for the adoption of mobile devices in the Australian health context may be intention, individual readiness, advantages, safety issues, features, self-efficacy, complexity, training, compatibility, social influences, age and gender.