According to the World Health Organization (WHO; 2011), mobile health (mHealth) solutions, defined as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices,” (p. 6) are becoming common in health fields to deliver behavioral change interventions to large demographics including older adults. Although rates of mobile phone adoption among persons age ≥65 in the United States still trail younger populations, current trends indicate that the penetration of mobile technology among older users is growing exponentially. The latest Pew survey showed a 24% increase in smartphone ownership rate in the past 3 years, with the current estimate of four in 10 older adults being smartphone owners (Anderson, 2016). This accelerating penetration rate makes mHealth interventions well suited for this demographic. Additional factors that might facilitate growth of mHealth interventions among users of all ages is that individuals tend to have their phones in close proximity to them all the time, making mobile phones a convenient venue for unobtrusive touch points with participants in their natural environment (Demiris et al., 2008). Finally, the rapidly evolving technical sophistication of mobile phones enables timely feedback, personalization, and interactivity features, all of which are important for effectiveness of behavioral interventions over time (Riley et al., 2011).
Despite these merits, to date, mHealth solutions are still underutilized in the deployment of interventions to promote health behaviors. Although several factors may bridge this implementation gap, an important step toward promoting mHealth intervention development and accelerating the rate of adoption within the scientific and clinical community is to synthesize existing evidence with respect to the effectiveness of mHealth interventions for eliciting behavioral change and to map behavioral change theories, techniques, and agents that might account for changes in the targeted behavior (Riddle, 2015; Riley et al., 2011). This improved understanding, anchored in empirical literature, may help accelerate the rate of scientific discovery and facilitate new and improved mHealth solutions that will have higher implementation potential.
Several reviews have examined the evidence of effectiveness of mHealth solutions in the context of targeting specific behaviors, such as physical activity, or specific conditions, such as chronic pain (Direito, Carraça, Rawstorn, Whittaker, & Maddison, 2017; Lalloo et al., 2017), and there has been a scoping review of mHealth technologies for managing chronic conditions in older adults (Matthew-Maich et al., 2016). In addition, there have been reviews of mHealth interventions focused generally on health behavior changes in younger and mixed demographics (Fedele, Cushing, Fritz, Amaro, & Ortega, 2017; Free et al., 2013; Payne, Lister, West, & Bernhardt, 2015; Zhao, Freeman, & Li, 2016). However, given that the health needs of older adults and their comfort with technology are different from other demographic segments, it is imperative to conduct a thorough review of mHealth behavioral change interventions investigating intervention effectiveness and focused on older populations.
The aims of the current review are to examine the effectiveness of mHealth interventions in eliciting health behavioral change across a broad range of health conditions among people age ≥60, and to examine the integration of behavioral change theories, techniques, and agents in these interventions.
The Arksey and O'Malley (2005) framework was used to guide this scoping review. The framework has five distinct stages: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; and (5) assembling, summarizing, and reporting the results.
Stage 1: Identifying the Research Question
The first stage included in-depth exploration of literature, followed by identification of knowledge gaps. As described in the introduction, a gap exists in the review literature on the effectiveness of mHealth interventions to promote behavioral change in older adults. Two main research objectives were defined: (a) synthesize the evidence on the effectiveness of mHealth solutions to elicit behavioral change in adults age ≥60 (international convention for old age [Organization of American States, 2009]); and (b) describe the integration of behavioral change theories, techniques, and agents in mHealth interventions.
Stage 2: Identifying Relevant Studies/Search Strategy
The literature search was performed by two authors (O.Z., I.R.) and an expert librarian. The search was conducted in main health databases—PubMed, CINAHL, PsycINFO, and EMBASE. The keywords and/or combination of medical subject headings (MESH) were modified for each database to optimize search strategies. Specifically, keywords such as mobile, app, wearable devices, cellphone, handheld computers, and qualifiers for health behaviors such as health behavior, diet, exercise, and medication adherence informed search strategies. Lastly, two authors (O.Z., I.R.) independently verified search terms and discussed initial results to confirm that the strategies performed as expected.
Stage 3: Study Selection
All peer-reviewed original articles retrieved from the databases were considered eligible for further review. Conference proceedings, preprints, policy or hospital reports, and working papers were deemed grey literature and not included for further analyses.
Eligibility Criteria. Inclusion criteria were as follows: (a) smart mobile device (e.g., smartphone, tablet, wearable device); (b) age ≥60; (c) health behavior; (d) written in English; and (e) published in the past 10 years (April 2008–2018). Exclusion criteria were: (a) studies that validated the electronic version of scales or questionnaires from the existing instruments; (b) studies of smart device–based interventions in acute care; (c) studies that described the design, development, or usability evaluation of a smartphone-based mHealth intervention; and (d) studies that did not conform with the WHO definition of mHealth.
Following the initial search, two authors (O.Z., I.R.) independently reviewed the titles and abstracts to determine eligibility. Next, they independently retrieved and reviewed full text such that discrepancies in the selection could be mapped and summarized. Disagreement (in approximately 15% of studies) between the initial reviewers was arbitraged by a third reviewer (A.T.C.). The workflow was summarized using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (Figure 1).
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart.
Stage 4: Charting the Data
Microsoft® Excel 2016 and EndNote X7 were used to remove duplicates and build a summary database. A small subset of five studies was selected for a pilot of data extraction.
Stage 5: Assembling, Summarizing, and Reporting the Results
The identified studies were summarized according to the research objectives. Ambiguity was discussed at research team meetings. The information was abstracted with respect to the following features: author, research location, health context, study type, sample, intervention, retention, behavioral change theory, behavioral change technique cluster, behavioral change agents, and behavioral outcomes. Behavioral change theory is a set of interrelated concepts, definitions, and propositions that explain behavior and suggest ways to achieve behavioral change (Glanz, Rimer, & Viswanath, 2008). Behavioral change theories were extracted from each study if they were explicitly mentioned as guiding interventions. Behavioral change agents are putative mechanisms or processes that are hypothesized to be measurable and modifiable and play a causal role in producing behavior change (Nielsen et al., 2018). Examples of agents include psychological and social constructs such as self-efficacy, self-regulation, acceptance, stress reactivity, and social support. Behavioral change agents were extracted from each study if they were explicitly described as such in the studies, or if the constructs mentioned were consistent with behavioral change theories that informed the intervention and they were measured before and after the intervention. Behavioral change techniques (BCTs) such as feedback, self-monitoring, and reinforcement, are observable components of an intervention designed to effect causal processes that govern behavior, and behavioralchange clusters are groups of BCTs that share similar mechanisms of change (Michie et al., 2013). BCTs used in the interventions were coded according to the BCT taxonomy in Michie et al. (2013), which comprises 16 clusters (e.g., scheduling consequences, reward and threat, repetition and substitution, antecedents, associations).
The initial search returned 1,929 articles: 779 in PubMed, 522 in CINAHL, 633 in PsycINFO, and 131 in EMBASE. Following the PRISMA guidelines, duplicates were eliminated, and the titles and abstracts were screened, which narrowed the results to 539 articles. A full-text review reduced the sample to 37 articles due to age, behavioral outcome, and mobile exclusion criteria. After applying the additional WHO (2011) definition of mHealth exclusion criteria, 17 additional articles that used telehealth interventions were removed. The remaining 20 articles informed the analytical sample used for the review.
Characteristics of Included Studies
The 20 articles analyzed in the current review were organized under three themes according to target behaviors. Of these articles, 11 targeted physical activity, eight targeted disease and medication management, and one targeted diet. Because dietary interventions involve a different set of objectives than interventions targeting physical activity and disease and medication management, they were included in a separate category. In many studies, disease and medication management imply having a specific health condition versus dietary studies that often target overall metabolic health. The health contexts were overall health maintenance in otherwise robust individuals (n = 6); cardiovascular health (n = 5); chronic obstructive pulmonary disease (COPD; n = 4); dementia (n = 2); lung transplant (n = 1); cancer (n = 1); and cataracts (n = 1). The majority of studies were conducted in the United States (n = 9) and Europe (n = 8). The sample size of the interventions ranged from 4 to 521 participants, with an average of 102 (SD = 131) participants. Average age was 66 (SD = 5.6) years. The intervention duration ranged between 3 and 52 weeks, with a mean of 15 weeks (SD = 14 weeks).
Most studies were randomized controlled trials (RCTs) (n = 15); however, three studies used quasi-experimental designs, and two were multi-arm trials without random allocation. The lowest retention rate of 62% was in a study with patients with Alzheimer's disease; however, six of 20 studies had a 100% retention rate in intervention arms, with an average of 89% (SD = 11%) across studies.
Integration of Features Promoting Behavioral Change
Across studies, two main strategies were used to support behavioral change: behavioral change theories and BCTs. In total, six studies reported using specific behavioral change theories to support their interventions. The most commonly used theory was social cognitive theory (n = 5), whereas Orem's self-care theory was used in one study.
The most commonly used clusters of BCTs were feedback and monitoring (n = 12), associations (n = 8), goals and planning (n = 6), and social support (n = 5). Feedback and monitoring were in the form of self- or external-monitoring and receiving tailored feedback on a person's progress with respect to pre-specified goals. Associations were in the form of cueing the targeted behavior through either personalized or general text messages. Goals and planning were in the form of setting personalized goals and reviewing discrepancies between projected and actual behavioral goals. Social support was in the form of emotional or educational support from peers as well as health care providers.
Behavioral change agents, which influence the development and maintenance of the behavior of interest, were identified in six studies and included self-efficacy (n = 5) and a theory-specific construct, self-care agency (n = 1). Interestingly, four of six studies that included self-efficacy measures promoted physical activity, and two studies were in a disease and medication adherence context.
Effectiveness of Interventions and Features
Physical Activity. In total, 11 studies implemented interventions intended to improve physical activity (Table 1). Among these, three studies targeted increase in duration of moderate to vigorous physical activity (MVPA), three studies targeted increase in step count, and five other studies targeted overall increase in physical activity (e.g., exercise sessions, regular daily walking, moving intensity). Overall, the studies reported increases in these measures.
Characteristics of Studies Focusing on Physical Activity
On average, there was a 12 minutes per day increase in MVPA across studies, with the highest increase of approximately 25 minutes per day achieved in an 8-week PDA–enhanced intervention that collected self-report of physical activity and promoted individualized goal setting through visual and text feedback (King et al., 2008). The lowest increase of approximately 0.4 minutes per day was the result of an 8-week mobile application (app) intervention that monitored activity through smartphone-based accelerometry and provided “just-in-time” social normative feedback (King et al., 2016). In another study targeting patients with COPD and diabetes in which patients were monitored by actigraphy and a primary care nurse provided personalized digital feedback concerning attainment to pre-specified physical activity goals, a 10 minutes per day increase in MVPA was reported (Verwey et al., 2014).
A pooled estimate across studies that targeted step count showed an average 556 steps per day increase, with the highest gain of approximately 2,220 steps per day in an 8-week intervention that used daily actigraphy self-monitoring, goal setting, and bi-weekly counseling among community-dwelling older adults (Vidoni et al., 2016). Conversely, a decrease in approximately 1,000 steps per day was observed after a 6-month intervention in patients with COPD that used daily symptom and physical activity self-monitoring and weekly tailored reinforcement text messages from a research nurse (Nguyen, Gill, Wolpin, Steele, & Benditt, 2009). Another study that provided postmenopausal women with wearable activity trackers achieved approximately a 790 (SD = 1,979) steps per day increase over 16 weeks (Cadmus-Bertram, Marcus, Patterson, Parker, & Morey, 2015).
Five studies reported an increase in overall activity. One using weekly texts over 12 weeks reported an increase in exercise frequency among community-dwelling older adults (Müller, Khoo, & Morris, 2016). A study that used activity sensors, visual feedback, and frequent motivational texts increased activity counts per minute among patients with COPD (Tabak, op den Akker, & Hermens, 2014). A study that used an app for endurance walking exercise with syncantly in one participant, sleep chronous music pacing increased the likelihood of daily regular walks among patients with COPD (Liu et al., 2008). Another study that used a tablet app increased training adherence in a health maintenance context (Silveira et al., 2013), and lastly, a home monitoring system with a wearable component increased moving intensity in persons with Alzheimer's disease (Lazarou et al., 2016).
Disease and Medication Management. In total, six studies implemented interventions intended to improve medication adherence and two studies aimed to improve overall disease management (Table 2). All but one study (Spoelstra et al., 2016) reported an improvement in the targeted behavior.
Characteristics of Studies Focusing on Disease and Medication Management
Many studies focused on adherence to prescription medications, which was assessed through questions to evaluate the extent to which a person's behavior corresponds with the prescribed medication regimen or using standardized medication adherence questionnaires. One study included blood biomarkers to objectively measure drug metabolites (Quilici et al., 2013). On average, there was a 9% difference in adherence between intervention and control arms. The largest 23% within-group change was observed over 1 week in a study that used a messaging app (LINE) to deliver postoperative health information to patients after cataract surgery (Sanguansak et al., 2017), and the smallest within-group change of 8% was observed over 28 days in a study that used an app to improve therapy management in older cardiac patients (Mertens et al., 2016). Another study achieved 3% and 6% differences in subjective and objective measures of adherence, respectively, between intervention and control arms as a result of 1-month daily text messaging reminders to improve aspirin therapy (Quilici et al., 2013). Wald Bestwick, Raiman, Brendell, and Wald (2014) demonstrated a 16% between-group difference in blood pressure and lipid-lowering medication adherence after 6 months of text messaging to patients with cardiovascular risks. Similarly, Piette et al. (2015) demonstrated a between-group difference of approximately 14% in adherence after an enhanced intervention that used interactive voice response calls and summary reports to informal caregivers of patients with heart failure. Non-significant between- and within-group differences in adherence were observed in the study that sent daily text messages over 3 weeks to patients prescribed anticancer medications (Spoelstra et al., 2016).
Two studies that evaluated impact of mHealth interventions on disease management recruited patients with conditions that require complex medical regimens and used instruments that captured self-care behavior. Specifically, DeVito Dabbs et al. (2016) reported a 9% slower decline in self-care behaviors in patients after lung transplantation over 1 year in the study intervention arm that used a smartphone app allowing to record patients' daily health indicators, view trends, and receive automatic feedback if health indicators were outside of established parameters. Hägglund et al. (2015) showed a 35% improvement over 90 days in self-care behaviors in patients with heart failure when using a tablet connected to a digital scale, as compared to a 9% improvement in standard care.
Diet. One study targeted diet (Table 3). Atienza, King, Oliveira, Ahn, and Gardner (2008) reported an increase in vegetable servings, as well as a trend toward greater in-take of dietary fibers from grains over 8 weeks in response to a PDA–enhanced intervention that used an electronic food diary and individualized dietary goals. The servings of vegetables and fibers from grains increased from 1.5 servings/day/1,000 kcal to 2.5 servings/day/1,000 kcal in the intervention arm compared to steady consumption of approximately 1.8 servings/day/1,000 kcal in the control arm. The study did not include measures of change agents.
Characteristics of Study Focusing on Diet
A scoping review was performed of mHealth interventions to promote behavioral change, investigating intervention effectiveness focused on older populations. Of the 20 studies included in the review, the primary behavior targeted was increase in physical activity, particularly in the context of COPD and general health maintenance. The other main focus for behavioral change was disease and, particularly, medication management. These studies tended to be focused on the management of cardiovascular disease. There was only one study that focused specifically on diet. Although focusing on physical activity and medication management is important, it is perhaps important to also place greater focus on other health-promoting behaviors such as dietary management. A more holistic focus on health management, as opposed to medication management specifically, might also be an area for future emphasis.
BCTs that were used included goals and planning, feedback and monitoring, shaping knowledge, association, and social support. These BCTs were reasonable choices, as shaping knowledge, goals, planning and association, feedback, and monitoring can facilitate people's development of skills for acquiring and maintaining healthful lifestyles and routines. In particular, advances in newer technologies have made it possible for participants to obtain feedback through newer technologies, such as wearable devices, and engage in monitoring of their health status over time using visualizations (DeVito Dabbs et al., 2016). Social support was also incorporated into several physical activity interventions and one intervention focused on disease and medication management. There were some other clusters of BCTs that did not appear to be used, such as repetition and substitution, and antecedents (e.g., restructuring the physical environment, restructuring the social environment), but seem as if they might be useful for facilitating health behavior change in older adults. One cautious suggestion that could be made for future studies is to diversify the portfolio of the deployed BCTs so as to engage other behavioral change mechanisms.
A limited number of studies reported using a theoretical framework, and social cognitive theory was the only theory that was commonly used. In recent years, there has been recognition of the need to incorporate theoretical frameworks in mHealth technologies (Matthew-Maich et al., 2016), as well as the need to improve theory to inform the development of mobile interventions, particularly to integrate the real-time feedback that can now be collected through devices (Riley et al., 2011). In addition, the stage model recently promulgated by the National Institutes of Health calls for increased focus on mechanisms of change (Onken, Carroll, Shoham, Cuthbert, & Riddle, 2014; Riddle, 2015). As such, future studies should consider further specifying intervention features that promote behavioral change and testing whether an intervention worked by engaging specific hypothesized change mechanisms. Although self-efficacy was examined as a change agent in a small number of studies, evidence that the interventions did not improve self-efficacy suggests that other psychological change agents accounted for improvement in behaviors. Thus, there is a need to study other change agents in mHealth interventions for older adults.
There were various limitations to the current study. First, based on the Arksey and O'Malley framework, articles were identified that investigated the efficacy of mHealth interventions among older adults. The studies that were included in the sample had particular areas of emphasis, such as research that was conducted in developed countries and with particular disease foci (e.g., COPD, cardiovascular disease). As such, it is unclear whether the findings of the current review would pertain to other contexts, such as mHealth interventions in developing countries and interventions for other conditions, and these remain important topics for future research.
Given the nascent state of mHealth intervention literature for older adults, inclusive definitions were used in the coding of behavioral change theory, techniques, and agents for this initial scoping review. This inclusive coding scheme might have provided overly optimistic estimates of the behavioral change theory and agent use in this population. Given that theory guidance might support intervention efficacy, future studies should specifically evaluate whether authors considered a theoretical framework during study development. In addition, extant research has recognized the importance of considering age-related factors such as perception, cognition, and movement control in the design of technology for older adults (Fisk et al., 2018; Machado et al., 2018). The current review did not specifically examine whether/how these issues were addressed in the design and their potential association with the efficacy of the interventions, but consideration of these factors could be a fruitful direction for future research.
A scoping review was performed of mHealth interventions for adults age ≥60, with two goals: (a) to examine the effectiveness of mHealth interventions in eliciting health behavioral change across a broad range of health conditions and (b) to examine the integration of behavioral change theory, techniques, and agents in these interventions. Most interventions were targeted and reported increases in physical activity and disease and medication management. As such, the findings indicate that mHealth interventions are a promising and potentially effective route to promote behavioral change in older populations. Although BCT such as feedback and monitoring, associations, goals and planning, social support, and social cognitive theory constructs were commonly examined in these studies, there might be opportunity to incorporate other established theoretical models and techniques of health behavioral change. In summary, the current review contributes to extant literature on mHealth behavioral change interventions targeting older adults in two primary ways: (a) by elucidating the areas of emphasis in BCT and possibilities for future mHealth interventions; and (b) identifying gaps in the research literature.
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Characteristics of Studies Focusing on Physical Activity
|Study (Year) Location||Health Context||Study Type||Sample||Intervention||Retention||Behavioral Change|
|Cadmus-Bertram, Marcus, Patterson, Parker, & Morey (2015)United States||Health maintenance||RCT||N = 51Mean age = 600% male||For 16 weeks, participants were coached to monitor their progress with respect to individualized goals using online data from their activity tracking device||100%||SCT||Feedback and monitoring||NR||Total MVPA increased by 62 (SD = 108) minutes per week (p = 0.008); step count increased by 789 (SD = 1,979) steps/day (p = 0.01)|
|King et al. (2008)United States||Health maintenance||RCT||N = 37Mean age = 6057% male||For 8 weeks, participants documented and received visual feedback and automatic texts with respect to the reported physical activity levels using a PDA device||100%||SCT||Goals and planning; feedback and monitoring||NR||Moderate-intensity or MVPA changed from 123.9 (SD = 114.5) to 301.6 (SD = 298.3) minutes per week (p < 0.05)|
|King et al. (2016)United States||Health maintenance||RCT||N = 95Mean age = 6025% male||For 8 weeks, participants used social, affective, and analytic behavioral change apps that provided automated feedback and information with respect to activity data captured by actigraphy||94%||SCT||Goals and planning, feedback and monitoring, social support||NR||Mean differences of 3.2 (SE = 0.02), 1.4 (SE = 0.02), and 1.3 (SE = 0.02) in MVPA were observed for social, affect, and analytic behavioral change, respectively, compared to control arms|
|Lazarou et al. (2016)Greece||Dementia||Quasi-experimental||N = 4Mean age = 7425% male||For 3 to 4 months, participants' activity was monitored and visualized in terms of observed patterns and trends using wearable and environmental sensors||100%||NR||Feedback and monitoring||NR||Moving intensity increased significantly in one participant, sleep features improved in all four participants|
|Liu et al. (2008)Taiwan||COPD||RCT||N = 48Mean age = 72100% male||For 3 months, participants were encouraged to engage in daily intense walking by following the tempo of music from the mobile app and by having their adherence monitored daily||Unclear||NR||Monitoring, association||NR||In long term, 92% vs. 38% of participants in the intervention and control arms maintained regular endurance walking|
|Müller, Khoo, & Morris (2016)Malaysia||Health maintenance||RCT||N = 43Mean age = 6326% male||For 12 weeks, participants received daily automated text messages so as to increase adherence to a received exercise booklet||91%||SCT||Shaping knowledge, association||Self-efficacy decreased by 6.5 (SD = 19.7) and 9.9 (SD = 19.7) in intervention and control arms||Mean difference of 1.2 (95% CI [0.18, 2.24]) in exercises per week was observed in intervention vs. control arm|
|Nguyen, Gill, Wolpin, Steele, & Benditt (2009)United States||COPD||RCT||N = 17Mean age = 6835% male||For 6 months, participants received either weekly personalized or standard text messages from a research nurse concerning self-reported symptoms and exercise||94%||SCT||Shaping knowledge, association||Self-efficacy increased from 4.8 (SD = 1.6) to 6.0 (SD = 0.9) in one arm and decreased from 7.1 (SD = 0.6) to 6.2 (SD = 0.6) in the second arm||Total steps/day, % time active, and % active time at moderate high activity increased from 5,229 (SD = 1,068), 68.9 (SD = 4.3), and 19.1 (SD = 2.7), respectively, to 5,838 (SD = 1,096), 69.3 (SD = 4.4), and 23.5 (SD = 2.9), respectively, in one arm versus comparable decrease from 6,692 (SD = 1,007), 67.7 (SD = 4.1), and 27.1 (SD = 2.6), respectively, to 5,675 (SD = 1007), 69.5 (SD = 4.1), and 23.6 (SD = 2.6), respectively, in the second arm|
|Silveira et al. (2013)Switzerland||Health maintenance||Multi arm non-RCT||N = 44Mean age = 7536% male||For 12 weeks, participants were encouraged through individual motivational and social features to engage in twice weekly tablet app-based home training programs||75%||NR||Feedback and monitoring, association, social support||NR||Training adherence was 84% and 81% in individual motivational and social features, respectively, compared to 59% in control arm|
|Tabak, op den Akker, & Hermens (2014)The Netherlands||COPD||RCT||N =15 in intervention armMean age = 6660% male||For 3 weeks, participants received visual feedback and automatic text messages every 2 hours concerning the difference between their concurrent and baseline activity levels||93%||NR||Feedback and monitoring||NR||Total activity in terms of actigraphy counts per minute increased from 799 (SD = 280) to 903 (SD = 269) (p = 0.08)|
|Verwey et al. (2014)
The Netherlands||COPD Type 2 diabetes||Quasi-experimental||N =20Mean age = 6055% male||For 2 to 3 months, participants received personalized feedback from their primary care nurse concerning adherence to the prespecified physical activity goals monitored by actigraphy||85%||NR||Goals and planning, feedback and monitoring, social support||Self-efficacy changed from 3.14 (SD = 0.45) to 3.24 (SD = 0.43) (p = 0.02)||Moderate-intense physical activity changed from 29 (SD = 21) to 39 (SD = 24) minutes per day (p = 0.02)|
|Vidoni et al. (2016)
United States||Alzheimer's disease||RCT||N = 30Mean age = 71.544% male||For 8 weeks, participants received a personalized prescription booklet and feedback in the form of bi-weekly phone calls from the research coaches concerning goals of increasing step count monitored by actigraphy||57%||NR||Goals and planning, feedback and monitoring, social support||Self-efficacy decreased by 0.8 (SD = 3.7) and 0.3 (SD = 3.1) points in cognitively impaired and non-impaired arms, respectively||Average weekly step count increased insignificantly by 1,284 (SD = 12,976) steps in the cognitively impaired arm and significantly by 15,530 (SD = 18,950) steps in the non-impaired arm|
Characteristics of Studies Focusing on Disease and Medication Management
|Study (Year) Location||Health Context||Study Type||Sample||Intervention||Retention||Behavioral Change|
|DeVito Dabbs et al. (2016)United States||Lung transplantation||RCT||N = 201Mean age = 6255% male||For 12 months, participants received a smart-phone app that allowed them to monitor and visualize symptoms and health behaviors and received automatic alerts for abnormal values||100%||Orem's self-care theory||Feedback and monitoring, association||Postintervention self-care agency measures were similar between intervention and control arms||Postintervention decline in high adherence to medical regimen was similar in intervention and control arms: 33% vs. 42% (p > 0.05)|
|Hägglund et al. (2015)
Sweden||Heart failure||RCT||N = 72Mean age = 6868% male||For 3 months, participants received a tablet connected to a weight scale that allowed them to monitor, visualize, and receive personalized health information||100%||NR||Feedback and monitoring, shaping knowledge, social support||NR||Postintervention self-care improved by 35% in intervention vs. 9% in control arm (p < 0.05)|
|Mertens et al. (2016)Germany||CVD||Quasi-experimental||N = 24Mean age = 7550% male||For 1 month, participants received a tablet app with personalized automated reminders for medications||100%||NR||Shaping knowledge, association||NR||Postintervention medication adherence increased by 8%, from 50 (SD = 3.44) to 54 (SD = 2.01)|
|Piette et al. (2015)United States||Heart failure||RCT||N = 331Mean age = 6899% male||For 12 months, participants received weekly interactive voice response calls that allowed monitoring and tailored information delivery||89%||NR||Feedback and monitoring, shaping knowledge||NR||Postintervention adherence to heart failure medication improved in 12 months from 50.3% to 74.9% in one arm vs. 54.2% to 84.9% improvement in another arm (13.8%, 95% CI [3.7, 23.8], p = 0.01)|
|Quilici et al. (2013)France||CVD||RCT||N = 499Mean age = 6477% male||For 1-month, participants received daily personalized reminder to take aspirin||96%||NR||Shaping knowledge, association||NR||Postintervention self-reported and objective non-adherence measures were 2.8% vs. 7.2% and 5.2% vs. 11.2% for intervention and control arms, respectively (p < 0.05 for all)|
|Sanguansak et al. (2017)Thailand||Cataract||Multi-arm non-RCT||N = 98Mean age = 6654% male||For 1 week, participants received daily educational messages and reminders with respect to postoperative management||100%||NR||Shaping knowledge, association||NR||Postintervention adherence 7 days after surgery was 96% and 73% (p = 0.004) in the intervention and control arms, respectively; no differences in adherence were observed 30 days after surgery|
|Spoelstra et al. (2016)United States||Cancer||RCT||N = 75Mean age = 6045% male||For 21 days, participants received daily personalized automated reminders to take cancer medications||87%||SCT||Shaping knowledge, association||Postintervention self-efficacy measures were similar between intervention and control arms||Postintervention adherence measures were similar between intervention and control arms|
|Wald, Bestwick, Raiman, Brendell, & Wald (2014)United Kingdom||CVD||RCT||N = 301Median age = 6055% male||For 6 months, participants received automated personalized reminders to take medications. Reminders were sent daily for the first 2 weeks, twice weekly for the next 2 weeks, and weekly thereafter||99%||NR||Association||NR||Postintervention adherence was 16% higher (95% CI [7, 14]) in intervention vs. control arm (p < 0.001)|
Characteristics of Study Focusing on Diet
|Study (Year) Location||Health Context||Study Type||Sample||Intervention||Retention||Behavioral Change Theory|
|Atienza, King, Oliveira, Ahn, & Gardner (2008) United States||Health maintenance||RCT||N = 36Mean age = 6130% male||For 8 weeks, participants received a PDA device that allowed daily vegetable and whole grain consumption self-monitoring||75%||SCT||Goals and planning, monitoring||NR||Intervention participants had greater increase in vegetable servings (p = 0.02) and dietary fiber intake from grain (p = 0.1) compared to the control arm|