ObjectiveConsumers are living longer, creating more pressure on the health system and increasing their requirement for self-care of chronic conditions. Despite rapidly-increasing numbers of mobile health applications (‘apps’) for consumers’ self-care, there is a paucity of research into consumer engagement with electronic self-monitoring. This paper presents a qualitative exploration of how health consumers use apps for health monitoring, their perceived benefits from use of health apps, and suggestions for improvement of health apps.Materials and Methods‘Health app’ was defined as any commercially-available health or fitness app with capacity for self-monitoring. English-speaking consumers aged 18 years and older using any health app for self-monitoring were recruited for interview from the metropolitan area of Perth, Australia. The semi-structured interview guide comprised questions based on the Technology Acceptance Model, Health Information Technology Acceptance Model, and the Mobile Application Rating Scale, and is the only study to do so. These models also facilitated deductive thematic analysis of interview transcripts. Implicit and explicit responses not aligned to these models were analyzed inductively.ResultsTwenty-two consumers (15 female, seven male) participated, 13 of whom were aged 26–35 years. Eighteen participants reported on apps used on iPhones. Apps were used to monitor diabetes, asthma, depression, celiac disease, blood pressure, chronic migraine, pain management, menstrual cycle irregularity, and fitness. Most were used approximately weekly for several minutes per session, and prior to meeting initial milestones, with significantly decreased usage thereafter. Deductive and inductive thematic analysis reduced the data to four dominant themes: engagement in use of the app; technical functionality of the app; ease of use and design features; and management of consumers’ data.ConclusionsThe semi-structured interviews provided insight into usage, benefits and challenges of health monitoring using apps. Understanding the range of consumer experiences and expectations can inform design of health apps to encourage persistence in self-monitoring.

BackgroundTobacco smoking leads to death or disability and a drain on national resources. The literature suggests that cigarette smoking continues to be a major modifiable risk factor for a variety of diseases and that smokers aged 18-30 years are relatively resistant to antismoking messages due to their widely held belief that they will not be lifelong smokers.ObjectiveTo conduct a randomized controlled trial (RCT) of a computer-generated photoaging intervention to promote smoking cessation among young adult smokers within a community pharmacy setting.MethodsA trial was designed with 80% power based on the effect size observed in a published pilot study; 160 subjects were recruited (80 allocated to the control group and 80 to the intervention group) from 8 metropolitan community pharmacies located around Perth city center in Western Australia. All participants received standardized smoking cessation advice. The intervention group participants were also digitally photoaged by using the Internet-based APRIL Face Aging software so they could preview images of themselves as a lifelong smoker and as a nonsmoker. Due to the nature of the intervention, the participants and researcher could not be blinded to the study. The main outcome measure was quit attempts at 6-month follow-up, both self-reported and biochemically validated through testing for carbon monoxide (CO), and nicotine dependence assessed via the Fagerström scale.ResultsAt 6-month follow-up, 5 of 80 control group participants (6.3%) suggested they had quit smoking, but only 1 of 80 control group participants (1.3%) consented to, and was confirmed by, CO validation. In the intervention group, 22 of 80 participants (27.5%) reported quitting, with 11 of 80 participants (13.8%) confirmed by CO testing. This difference in biochemically confirmed quit attempts was statistically significant (χ2 1=9.0, P=.003). A repeated measures analysis suggested the average intervention group smoking dependence score had also significantly dropped compared to control participants (P<.001). These differences remained statistically significant after adjustment for small differences in gender distribution and nicotine dependence between the groups. The mean cost of implementing the intervention was estimated at AU $5.79 per participant. The incremental cost-effectiveness ratio was AU $46 per additional quitter. The mean cost that participants indicated they were willing to pay for the digital aging service was AU $20.25 (SD 15.32).ConclusionsDemonstrating the detrimental effects on facial physical appearance by using a computer-generated simulation may be both effective and cost-effective at persuading young adult smokers to quit.Trial RegistrationAustralian New Zealand Clinical Trials Registry: ACTRN12609000885291; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12609000885291 (Archived by WebCite at http://www.webcitation.org/6F2kMt3kC)

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Purpose – Employees in community pharmacies play a far significant and distinct role compared to the employees in traditional retail stores. The purpose of this paper is to examine the effects of employee performance (EP) on customer loyalty of pharmacy services. Design/methodology/approach – Data were collected through a self-administered survey filled in by the customers of 25 community pharmacies. A total of 679 completely filled-in questionnaires were analysed. The proposed model was tested through structural equation modelling using AMOS 22. Findings – EP positively affects pharmacy customers’ perceived value (PV), trust and loyalty. PV and trust fully mediates the relationships between EP and customers’ attitudinal and behavioural loyalty. Unlike short-term customers, the long-term relational customers’ PV was found to have significant impact on their trust and behavioural loyalty. Research limitations/implications – This study is based on the Australian community pharmacy industries; hence, caution must be exercised in the generalization of the results to other countries. The study has considered only PV and trust in examining the link between the EP and customer loyalty. Other variables such as commitment could possibly influence the link, which has not been considered in this study. Originality/value – The study contributes to the existing literature by focusing on how EP affects both attitudinal and behavioural loyalty of pharmacy customers. It shows empirical evidence that EP influences customers’ PV and trust en-route to influencing their loyalty. The study measures EP based on both empathy and service provider performance covering a broader spectrum of the construct.

This study reports how showing a person an illustration of themselves following future weight-loss might impact on their actual future weight-loss. Weight was recorded weekly, 8 weeks before and 8 weeks after the intervention. A significant proportion of the 44 participants lost weight following the intervention: 17 vs. 29 (P = 0.01, chi-squared = 6.559). After the first 8 weeks, the mean change in weight was -0.32 kg [standard deviation (SD): 2.2, P = 0.37]. The weight change after the second 8 weeks was -0.94 kg (SD: 1.7, P = 0.001). The mean difference in weight losses between the two periods was -0.62 kg (SD: 2.1, P = 0.08).