Wearable Activity Monitor Use Is Associated With the Aerobic Physical Activity Guidelines and Walking Among Older Adults

Effect of Intrapersonal and Interpersonal Behavior Change Strategies on Physical Activity Among Older Adults: A Randomized Clinical Trial

Importance

Despite guidelines that recommend physical activity (PA), little is known about which types of behavior change strategies (BCSs) effectively promote sustained increases in PA in older adults who are insufficiently active.

Objective

To determine whether intrapersonal BCSs (eg, goal setting) or interpersonal BCSs (eg, peer-to-peer sharing or learning) combined with the Otago Exercise Program (17 strength and balance exercises and a walking program that are learned and individually tailored, with instruction to perform 3 times per week at home or location of choice) and a wearable PA monitor help older adults sustain increases in their PA.

Design, Setting, and Participants

This 2 × 2 factorial randomized clinical trial (Community-Based Intervention Effects on Older Adults' Physical Activity) of community-dwelling older adults 70 years or older with PA levels below minimum national PA guidelines was conducted in urban community centers. Dates of enrollment were from November 17, 2017, to June 15, 2021, with final follow-up assessments completed on September 2, 2022.

Interventions

Participants were randomized to intrapersonal (eg, goal setting) BCSs, interpersonal (eg, problem-solving with peer-to-peer sharing and learning) BCSs, intrapersonal and interpersonal BCSs, or an attention control group. All interventions included a PA monitor and 8 weekly small-group meetings with discussion, practice, and instructions to implement the exercise program and relevant BCSs independently between meetings and after the intervention.

Main Outcomes and Measures

The primary outcome was daily minutes of objectively measured total PA (light, moderate, or vigorous intensities) averaged over 7 to 10 days, measured at baseline and after the intervention at 1 week, 6 months, and 12 months.

Results

Among 309 participants (mean [SD] age, 77.4 [5.0] years; 240 women [77.7%]), 305 (98.7%) completed the intervention, and 302 (97.7%) had complete data. Participants receiving PA interventions with interpersonal BCS components exhibited greater increases in total PA than did those who did not at 1 week (204 vs 177 PA minutes per day; adjusted difference, 27.1 [95% CI, 17.2-37.0]; P < .001), 6 months (195 vs 175 PA minutes per day; adjusted difference, 20.8 [95% CI, 10.0-31.6]; P < .001), and 12 months (195 vs 168 PA minutes per day; adjusted difference, 27.5 [95% CI, 16.2-38.8]; P < .001) after the intervention. Compared with participants who did not receive interventions with intrapersonal BCS components, participants who received intrapersonal BCSs exhibited no significant changes in total PA at 1 week (192 vs 190 PA minutes per day; adjusted difference, 1.8 [95% CI, -8.6 to 12.2]; P = .73), 6 months (183 vs 187 PA minutes per day; adjusted difference, -3.9 [95% CI, -15.0 to 7.1]; P = .49), or 12 months (177 vs 186 PA minutes per day; adjusted difference, -8.8 [95% CI, -20.5 to 2.9]; P = .14) after the intervention. Interactions between intrapersonal and interpersonal BCSs were not significant.

Conclusions and Relevance

In this randomized clinical trial, older adults with low levels of PA who received interpersonal BCSs, the exercise program, and a PA monitor exhibited significant increases in their PA for up to 12 months after the intervention. Intrapersonal BCSs elicited no significant PA changes and did not interact with interpersonal BCSs. Our findings suggest that because effects of a PA intervention on sustained increases in older adults' PA were augmented with interpersonal but not intrapersonal BCSs, approaches to disseminating and implementing the intervention should be considered.

Trial Registration

ClinicalTrials.gov Identifier: NCT03326141.

Technical Assistance Received by Older Adults to Use Commercially Available Physical Activity Monitors (Ready Steady 3.0 Trial): Ad-Hoc Descriptive Longitudinal Study

Background

Despite evidence that regular physical activity (PA) among older adults confers numerous health and functional benefits, PA participation rates are low. Using commercially available wearable PA monitors (PAMs) is one way to augment PA promotion efforts. However, while expert recommendations exist for the specific information needed at the beginning of PAM ownership and the general ongoing need for structures that support as-needed technical troubleshooting, information is lacking about the type, frequency, and modes of assistance needed during initial and long-term ownership.

Objective

This paper describes problems reported and technical assistance received by older adults who used PAMs during the 18 months they participated in a community-based PA trial: Ready Steady 3.0 (RS3).

Methods

This was an ad-hoc longitudinal analysis of process variables representing technical problems reported and assistance received by 113 RS3 study participants in the 18 months after their orientation to PAMs. Variables included date of contact, problem(s) reported, mode of technical assistance, and whether the equipment was replaced. The descriptive analysis included frequencies and incidence rates of distinct contacts, types of problems, and technical assistance modes.

Results

On average, participants were aged 77 (SD 5.2) years. Most identified as female (n=87, 77%), reported experience using smartphones (n=92, 81.4%), and used the PAM between 2 and 18 months. Eighty-two participants (72.6%) reported between 1 to 9 problems with using PAMs, resulting in a total of 150 technical assistance contacts with a mean of 1.3 (SD 1.3) contacts. The incidence rate of new, distinct contacts for technical assistance was 99 per 100 persons per year from 2018 to 2021. The most common problems were wearing the PAM (n=43, 28.7%), reading its display (n=23, 15.3%), logging into its app (n=20, 13.3%), charging it (n=18, 12%), and synchronizing it to the app (n=16, 10.7%). The modalities of technical assistance were in person (n=53, 35.3%), by telephone (n=51, 34%), by email (n=25, 16.7%), and by postal mail (n=21, 14%).

Conclusions

In general, the results of this study show that after receiving orientation to PAMs, problems such as uncomfortable wristbands, difficulty using the PAM or its related app, and obtaining or interpreting relevant personal data were occasionally reported by participants in RS3. Trained staff helped participants troubleshoot and solve these technical problems primarily in person or by phone. Results also underscore the importance of involving older adults in the design, usability testing, and supportive material development processes to prevent technical problems for the initial and ongoing use of PAMs. Clinicians and researchers should further assess technical assistance needed by older adults, accounting for variations in PAM models and wear time, while investigating additional assistance strategies, such as proactive support, short GIF videos, and video calls.

Association Between Wearable Device Use and Levels of Physical Activity Among Older Adults in the US: Evidence From the 2019-2020 Health Information National Trends Survey

Objective To examine the relationship between electronic wearable device (WD) use and physical activity (PA) levels among older adults in the US. Methods Data were pooled from 3310 older adults from the 2019 and 2020 Health Information National Trends Survey. The explanatory variable was WD use, and the outcomes were weekly PA levels, resistance training, and sedentary time. Logistic regression was conducted to investigate the association between WD use and the reported outcome variables. Separate logistic models were also fitted to explore the relationship between WD use and physical activity outcomes among a subgroup of older adults with chronic conditions. Results A total of 14.4% of older adults reported WD use. Older adults who use WD were more likely to meet national guidelines for weekly levels of PA (odds ratio (OR) 1.60, 95% confidence intervals (CI) (1.10, 2.32); p = 0.015) and resistance strength training (OR 1.54, 95% CI (1.14, 2.09); p = 0.005) when compared with their counterparts not using WD. After restricting the analysis to those with chronic conditions only, WD use was only associated with a higher level of weekly strength training (OR 1.68, 95% CI 1.19, 2.38; p = 0.004). Conclusion  WD use may be associated with increased physical activity among older adults, including those with chronic health conditions. Further studies are needed to examine the factors influencing the adoption and sustained use of WD in older adults.

Better Understanding Wearable Activity Monitor Use and Non-Use Among Older Adults: A Qualitative Study

We used qualitative methods to better understand wearable activity monitor (WAM) use and its relationship with physical activity (PA) among older adults. Four focus groups, stratified by current/past ("ever") WAM users and "never" WAM users, were conducted with 57 participants aged ≥60 years. Data were coded with deductive and inductive approaches. Among ever WAM users, those with low pre-WAM PA levels reported higher PA related to WAM use while those with high pre-WAM PA levels reported they used WAM for tracking and monitoring their activity. Ever WAM users were motivated to increase PA through rewards and social support. Many never WAM users reported that WAM was unnecessary. WAM may be helpful for some older adults to increase their PA, particularly those who are motivated by setting and achieving PA goals. The results could help develop strategies to overcome barriers to WAM adoption and promote WAM use among older adults.

A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study

Background

Over half of US adults have at least one chronic disease, including obesity. Although physical activity is an important component of chronic disease self-management, few reach the recommended physical activity goals. Individuals who identify as racial and ethnic minorities are disproportionally affected by chronic diseases and physical inactivity. Interventions using consumer-based wearable devices have shown promise for increasing physical activity among patients with chronic diseases; however, populations with the most to gain, such as minorities, have been poorly represented to date.

Objective

This study aims to assess the feasibility, acceptability, and preliminary outcomes of an 8-week text-based coaching and Fitbit program aimed at increasing the number of steps in a predominantly overweight ethnic minority population.

Methods

Overweight patients (BMI >25 kg/m²) were recruited from an internal medicine clinic located in an inner-city academic medical center. Fitbit devices were provided. Using 2-way SMS text messaging, health coaches (HCs) guided patients to establish weekly step goals that were specific, measurable, attainable, realistic, and time-bound. SMS text messaging and Fitbit activities were managed using a custom-designed app. Program feasibility was assessed via the recruitment rate, retention rate (the proportion of eligible participants completing the 8-week program), and patient engagement (based on the number of weekly text message goals set with the HC across the 8-week period). Acceptability was assessed using a qualitative, summative evaluation. Exploratory statistical analysis included evaluating the average weekly steps in week 1 compared with week 8 using a paired t test (2-tailed) and modeling daily steps over time using a linear mixed model.

Results

Of the 33 patients initially screened; 30 (91%) patients were enrolled in the study. At baseline, the average BMI was 39.3 (SD 9.3) kg/m², with 70% (23/33) of participants presenting as obese. A total of 30% (9/30) of participants self-rated their health as either fair or poor, and 73% (22/30) of participants set up ≥6 weekly goals across the 8-week program. In total, 93% (28/30) of participants completed a qualitative summative evaluation, and 10 themes emerged from the evaluation: patient motivation, convenient SMS text messaging experience, social support, supportive accountability, technology support, self-determined goals, achievable goals, feedback from Fitbit, challenges, and habit formation. There was no significant group change in the average weekly steps for week 1 compared with week 8 (mean difference 7.26, SD 6209.3; P=.99). However, 17% (5/30) of participants showed a significant increase in their daily steps.

Conclusions

Overall, the results demonstrate the feasibility and acceptability of a remotely delivered walking study that included an HC; SMS text messaging; a wearable device (Fitbit); and specific, measurable, attainable, realistic, and time-bound goals within an ethnic minority patient population. Results support further development and testing in larger samples to explore efficacy.

What drives the use of wearable healthcare devices? A cross-country comparison between the US and Korea

Objective

Given the rapid growth of the wearable healthcare device market, we examined the associations among health-related and technology-related characteristics of using wearable healthcare devices and demonstrated how the associations differ between the US and Korean users.

Methods

Online self-administered surveys were conducted with 4098 participants (3035 in the US and 1063 in Korea) who were recruited through two online survey service providers based on quota sampling. The primary outcome was the use of wearable healthcare devices. Seven health-related, two technology-related, and five socio-demographic factors were included as explanatory variables. Binary logistic regression analyses and a Chow test were conducted.

Results

The health-related characteristics that were significantly associated with using wearable healthcare devices included disease-related worries (β = 0.11**), health information seeking (β = 0.26***), physical activity (β = 0.62***), and health-related expenditures ($50–$199, β = 0.38***; $200 or more, β = 0.56***). Hedonic (β = 0.33***), social (β = 0.31***), and cognitive innovativeness (β = 0.14*) also exhibited positive relationships. Younger, higher earner, and individuals with a child were more likely to use wearable healthcare devices. However, for Korean users, several associations disappeared including health information seeking, hedonic and social innovativeness, age, and household income.

Conclusions

Key drivers of using wearable healthcare devices include greater concern about a specific illness, active engagement in health-promoting behaviors, and hedonic and social motivation to adopt new technologies. However, more country-specific considerations are needed in future studies to identify the main benefits for target markets.

Implementing the physical activity vital sign in an academic preventive cardiology clinic

The aims were to implement physical activity (PA) screening as part of the electronic kiosk check-in process in an adult preventive cardiology clinic and assess factors related to patients' self-reported PA. The 3-question physical activity vital sign (PAVS) was embedded in the Epic electronic medical record and included how many days, minutes and intensity (light, moderate, vigorous) of PA patients conducted on average. This is a data analysis of PAVS data over a 60-day period. We conducted multivariable logistic regression to identify factors associated with not meeting current PA recommendations. Over 60 days, a total of 1322 patients checked into the clinic using the kiosk and 72% (n = 951) completed the PAVS at the kiosk. The majority of those patients were male (58%) and White (71%) with a mean age of 64 ± 15 years. Of the 951 patients completing the PAVS, 10% reported no PA, 55% reported some PA, and 35% reported achieving at least 150 min moderate or 75 min vigorous PA/week. In the logistic model, females (AOR = 1.4, 95%CI: 1.002-1.8, p = .049) vs. males, being Black (AOR = 2.0, 95%CI: 1.04-3.7, p = .038) or 'Other' race (AOR = 1.5, 95%CI: 1.02-2.3, p = .035) vs. White, unknown or other types of relationships (AOR = 0.0.26, 95%CI: 0.10-0.68, p = .006) vs. being married/partnered, and those who were retired (AOR = 1.9, 95% CI: 1.4-2.8, p < .001) or unemployed (AOR = 2.2, 95%CI: 1.3-3.7, p = .002) vs. full-time workers were associated with not achieving recommended levels of PA. The PAVS is a feasible electronic tool for quickly assessing PA and may prompt providers to counsel on this CVD risk factor.