Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study

Abstract

Background Although healthy aging can be stimulated by the reduction of sedentary behavior, few interventions are available for older adults. Previous studies suggest that self-monitoring might be a promising behavior change technique to reduce older adults’ sedentary behavior. However, little is known about older adults’ experiences with a self-monitoring–based intervention aimed at the reduction of sedentary behavior. Objective The aim of this study is to evaluate engagement, acceptability, usability, and preliminary efficacy of a self-monitoring–based mHealth intervention developed to reduce older adults’ sedentary behavior. Methods A mixed methods study was performed among 28 community-dwelling older adults living in Flanders, Belgium. The 3-week intervention consisted of general sedentary behavior information as well as visual and tactile feedback on participants’ sedentary behavior. Semistructured interviews were conducted to explore engagement with, and acceptability and usability of, the intervention. Sitting time was measured using the thigh-worn activPAL (PAL Technologies) accelerometer before and after the intervention. System usage data of the app were recorded. Quantitative data were analyzed using descriptive statistics and paired-samples t tests; qualitative data were thematically analyzed and presented using pen profiles. Results Participants mainly reported positive feelings regarding the intervention, referring to it as motivating, surprising, and interesting. They commonly reported that the intervention changed their thinking (ie, they became more aware of their sedentary behavior) but not their actual behavior. There were mixed opinions on the kind of feedback (ie, tactile vs visual) that they preferred. The intervention was considered easy to use, and the design was described as clear. Some problems were noticed regarding attaching and wearing the self-monitoring device. System usage data showed that the median frequency of consulting the app widely differed among participants, ranging from 0 to 20 times a day. No significant reductions were found in objectively measured sitting time. Conclusions Although the intervention was well perceived by the majority of older adults, no reductions in sitting time were found. Possible explanations for the lack of reductions might be the short intervention duration or the fact that only bringing the habitual sedentary behavior into conscious awareness might not be sufficient to achieve behavior change. Trial Registration ClinicalTrials.gov NCT04003324; https://tinyurl.com/y2p4g8hx

Exploring Breaks in Sedentary Behavior of Older Adults Immediately After Receiving Personalized Haptic Feedback: Intervention Study

“…This study reports on part of a larger mixed methods study being conducted to evaluate a self-monitoring mHealth intervention to reduce sedentary behavior in older adults [16]. The study was registered at ClinicalTrials.gov (identification number: NCT04003324) and was approved by the Committee of Medical Ethics of the Ghent University Hospital (Belgian registration number 2019/0398).…”

“…To be eligible for the current study, participants needed to (1) be at least 60 years old, (2) be Dutch-speaking, (3) be able to walk 100 meters without severe difficulties, and (4) have a smartphone. A detailed description of the study procedure has been published elsewhere [16]. Briefly, baseline data, including sociodemographic characteristics, were collected before the start of the intervention.…”

Usability of Rapid Cholera Detection Device (OmniVis) for Water Quality Workers in Bangladesh: Iterative Convergent Mixed Methods Study

“…While many rapid pathogen detection technologies have undergone validity analyses, there is a literature gap of usability analyses for such technologies [14][15][16][17][18]. Many usability studies instead evaluate technologies that aid interventions such as behavioral change, treatment adherence, or clinical decision making [23][24][25][26]. Thus, this study can serve as a model of iterative convergent mixed methods research design for device development to further promote usability analysis of rapid pathogen detection technologies and produce rapid detection devices that better fit into user workflows for increased uptake.…”

“…While the field of mHealth and rapid diagnostic testing for infectious disease is fast-growing, few rapid pathogen detection technologies have been developed for environmental surveillance [12,22]. Moreover, many mHealth usability studies have evaluated interventions such as behavioral change, medication adherence, or clinical decision-making technologies, but there is a literature gap for usability studies evaluating rapid pathogen detection technology [23][24][25][26]. By providing input in the device development process, end users can become cocreators and feel ownership in the device design, enabling greater uptake and integration into workflows.…”

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial

“…Older adults have had high levels of engagement with mHealth interventions and, in one study, have demonstrated higher levels of engagement with digital diabetes self-management than their younger counterparts. 23 , 24 Despite these promising studies, there is still a lack of research on longitudinal engagement with self-management technologies by those with multiple chronic conditions, with a view to understanding how best to facilitate and promote sustained engagement to maximise benefits.…”