The Mobile Phone Affinity Scale: Enhancement and Refinement

Abstract

BackgroundExisting instruments that assess individuals’ relationships with mobile phones tend to focus on negative constructs such as addiction or dependence, and appear to assume that high mobile phone use reflects pathology. Mobile phones can be beneficial for health behavior change, disease management, work productivity, and social connections, so there is a need for an instrument that provides a more balanced assessment of the various aspects of individuals’ relationships with mobile phones.ObjectiveThe purpose of this research was to develop, revise, and validate the Mobile Phone Affinity Scale, a multi-scale instrument designed to assess key factors associated with mobile phone use.MethodsParticipants (N=1058, mean age 33) were recruited from Amazon Mechanical Turk between March and April of 2016 to complete a survey that assessed participants’ mobile phone attitudes and use, anxious and depressive symptoms, and resilience.ResultsConfirmatory factor analysis supported a 6-factor model. The final measure consisted of 24 items, with 4 items on each of 6 factors: Connectedness, Productivity, Empowerment, Anxious Attachment, Addiction, and Continuous Use. The subscales demonstrated strong internal consistency (Cronbach alpha range=0.76-0.88, mean 0.83), and high item factor loadings (range=0.57-0.87, mean 0.75). Tests for validity further demonstrated support for the individual subscales.ConclusionsMobile phone affinity may have an important impact in the development and effectiveness of mobile health interventions, and continued research is needed to assess its predictive ability in health behavior change interventions delivered via mobile phones.

African American women’s relationship with their mobile phone, and what they want in a mobile delivered physical activity intervention: guidance for intervention development

“…The MPAS is a psychometrically supported measure that consists of six constructs representing both positive and negative cognitions and behaviors related to mobile phone use (25,26). The three positive subscales are Connectedness, Productivity and Empowerment/Safety, and respectively, they measure individuals' use of this technology to: (I) remain connected with friends and family (e.g., "My phone helps me stay close to family and friends"); (II) organize work/school schedule and/or related tasks (e.g., "My phone helps me stay up-to-date with work/school activities"); and (III) the ability to access help when in an unsafe situation (e.g., "Having my phone with me makes it easier to leave a risky situation") (25).…”

“…Data on how AA women use their phones as part of their daily routine may provide essential information about the mHealth components that are most relevant to help them adopt and maintain regular PA. There is research to indicate that an individual's daily pattern of mobile use as well as how frequently they carry this device with them may impact their receptivity to, and engagement with, interventions delivered through the mobile phone (25,26). As mentioned, there is limited research on the nature of the relationship that AA women have with the mobile phone.…”

Feasibility, acceptability, and validity of crowdsourcing for collecting longitudinal alcohol use data

“…More recently, researchers in addiction science have also begun to utilize mTurk. These studies have spanned a diverse range of theoretical perspectives and methods including, but not limited to, behavioral economics (e.g., Bickel, Jarmolowicz, Mueller, Koffarnus, & Gatchalian, 2012;Johnson, Herrmann, & Johnson, 2015;Kaplan et al, 2017;Morris et al, 2017;Peters, Rosenberry, Schauer, O'Grady, & Johnson, 2017), tobacco control policy (e.g., Lazard et al, 2017;Pearson et al, 2016;Shi, Wang, Emery, Sheerin, & Romer, 2017), behavioral addictions (e.g., Bock et al, 2016;Gearhardt, Corbin, & Brownell, 2016), public opinion related to addiction-related policy (e.g., Huhn, Tompkins, & Dunn, 2017;Rudski, 2016;Wen, Higgins, Xie, & Epstein, 2016), and measure development (e.g., Dunn, Barrett, Herrmann et al, 2016;Dunn, Barrett, Yepez-Laubach et al, 2016;Lac & Berger, 2013). Existing evidence supports the reliability and validity of common substance use scales when used on mTurk (e.g., the Alcohol Use Disorder Identification Test [AUDIT]; Kim & Hodgins, 2017).…”

Mapping of Crowdsourcing in Health: Systematic Review (Preprint)

“…Indeed, many health fields have since used crowdsourcing, with 20 medical fields identified in our systematic review compared with 8 fields in the Ranard et al's study [5]. Moreover, crowdsourcing use is still growing, as shown by the 11 articles published in Journal of Medical Internet Research since our last search date, mainly involving a survey task (9/11, 82%) [217][218][219][220][221][222][223][224][225][226][227]. Our study has some limitations.…”