Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework

Abstract

Background Mobile health (mHealth) is widely used as an innovative approach to delivering physical activity (PA) programs. Users’ adherence to mHealth programs is important to ensure the effectiveness of mHealth-based programs. Objective Our primary aim was to review the literature on the methods used to assess adherence, factors that could affect users’ adherence, and the investigation of the association between adherence and health outcomes. Our secondary aim was to develop a framework to understand the role of adherence in influencing the effectiveness of mHealth PA programs. Methods MEDLINE, PsycINFO, EMBASE, and CINAHL databases were searched to identify studies that evaluated the use of mHealth to promote PA in adults aged ≥18 years. We used critical interpretive synthesis methods to summarize the data collected. Results In total, 54 papers were included in this review. We identified 31 specific adherence measurement methods, which were summarized into 8 indicators; these indicators were mapped to 4 dimensions: length, breadth, depth, and interaction. Users’ characteristics (5 factors), technology-related factors (12 factors), and contextual factors (1 factor) were reported to have impacts on adherence. The included studies reveal that adherence is significantly associated with intervention outcomes, including health behaviors, psychological indicators, and clinical indicators. A framework was developed based on these review findings. Conclusions This study developed an adherence framework linking together the adherence predictors, comprehensive adherence assessment, and clinical effectiveness. This framework could provide evidence for measuring adherence comprehensively and guide further studies on adherence to mHealth-based PA interventions. Future research should validate the utility of this proposed framework.

Extended reality for mental health: Current trends and future challenges

“…One of the main issues with current e-Health applications and technological interventions is the lack of motivation for patients to maintain a sustained and periodic use of the technology (Yang et al, 2022). Among the factors that cause this lack of interest are not perceiving the usefulness of the intervention and poor adaptation to the users' level of expertise with the technology leading to frustration (Donkin and Glozier, 2012;Nickbakht et al, 2020).…”

“…On the other side, there are applications and platforms that gather information from the user's patterns in the background without actively trying to engage the user in any kind of interaction. Active participation of patients in their therapeutic activities is essential to improve the impact that technological advances might have on these interventions (Yang et al, 2022). Extended reality scenarios offer an auspicious opportunity in this regard: the user has to participate in the activities actively, but the application can extract information from their performance transparently and unobtrusively.…”

Impact of adherence to a lifestyle-integrated programme on physical function and behavioural complexity in young older adults at risk of functional decline: a multicentre RCT secondary analysis

“…To date, there is no consensus regarding which adherence measurement tool should be used, nor when and how adherence should be measured. [5][6][7] Besides the difficulty of recording adherence rates, achieving sufficiently high adherence is challenging. High adherence is frequently documented in the early stages of an exercise intervention, regardless of delivery mode and exact definition used.…”

“…Interventions Six-month trainer-supported aLiFE or eLiFE compared with a control group, which received written PA advice. Outcome measures Self-reporting adherence per month using a single question and after 6-month intervention using the Exercise Adherence Rating Scale (EARS, score range [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Treatment outcomes included function and disability scores (measured using the Late-Life Function and Disability Index) and sensor-derived physical behaviour complexity measure.…”

“…To date, there is no consensus regarding which adherence measurement tool should be used, nor when and how adherence should be measured 5–7. Besides the difficulty of recording adherence rates, achieving sufficiently high adherence is challenging.…”