Dataset Information

Use of a Wearable Technology and Motivational Interviews to Improve Sleep in Older Adults With Osteoarthritis and Sleep Disturbance: A Pilot Study.

ABSTRACT: The feasibility and preliminary efficacy of a mobile health self-management intervention aimed at improving sleep among older adults with osteoarthritis and disturbed sleep were evaluated. This was a one-group pre-/posttest pilot study. Feasibility was measured by the number of participants eligible, enrolled, and retained. Primary efficacy outcomes were Insomnia Severity Index (ISI) score and two sleep actigraphy variables: total sleep time (TST) and sleep efficiency (SE). Overall step count, self-efficacy (SEff), and acceptance of sleep difficulties (ASD) were mechanisms of action variables. Assessments were at baseline, Week 14 (postintervention), and Week 19 (follow up). Mixed effect models were used to measure change over time. Twenty-four participants (mean age = 71 years) were enrolled and 22 completed the study. Improvements of 1.2 (95% confidence interval [CI] [-2.43, -0.05]; p = 0.04) and 2.5 (95% CI [0.9, 4.9]; p = 0.02) points in the ISI and ASD scores, respectively, were found over the 19-week period. These findings add to a growing literature that suggests older adults might reap benefits from mobile health interventions. TARGETS:Older adults with osteoarthritis and insomnia symptoms. INTERVENTION DESCRIPTION:Activity trackers synced to a dashboard that triggered personalized weekly step goals and motivational messages augmented by telephone motivational interviews. MECHANISM OF ACTION:Physical activity, SEff, and ASD. OUTCOMES:Sleep measures. [Res Gerontol Nurs. 2019; 12(4):167-173.].

SUBMITTER: Zaslavsky O

PROVIDER: S-EPMC7048171 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Use of a Wearable Technology and Motivational Interviews to Improve Sleep in Older Adults With Osteoarthritis and Sleep Disturbance: A Pilot Study.

Zaslavsky Oleg O Thompson Hilaire J HJ McCurry Susan M SM Landis Carol A CA Kitsiou Spyros S Ward Teresa M TM Heitkemper Margaret M MM Demiris George G

Research in gerontological nursing 20190325 4

The feasibility and preliminary efficacy of a mobile health self-management intervention aimed at improving sleep among older adults with osteoarthritis and disturbed sleep were evaluated. This was a one-group pre-/posttest pilot study. Feasibility was measured by the number of participants eligible, enrolled, and retained. Primary efficacy outcomes were Insomnia Severity Index (ISI) score and two sleep actigraphy variables: total sleep time (TST) and sleep efficiency (SE). Overall step count, s ...[more]

PMID: 30901479

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Project description:BackgroundWearables and artificial intelligence (AI)-powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior's change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors.ObjectiveThe goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities.MethodsData from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (-CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time.ResultsThe residents of the +CP and -CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the -CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively.ConclusionsThe AI-powered digital health platform provides the community staff with actionable information regarding each resident's activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.

Project description:BACKGROUND:As adults age, their physical activity decreases and sedentary behavior increases, leading to increased risk of negative health outcomes. Wearable electronic activity monitors have shown promise for delivering effective behavior change techniques. However, little is known about the feasibility and acceptability of non-Fitbit wearables (Fitbit, Inc, San Francisco, California) combined with telephone counseling among adults aged more than 55 years. OBJECTIVE:The purpose of our study was to determine the feasibility, acceptability, and effect on physical activity of an intervention combining a wearable physical activity monitor, tablet device, and telephone counseling among adults aged 55-79 years. METHODS:Adults (N=40, aged 55-79 years, body mass index=25-35, <60 min of activity per week) were randomized to receive a 12-week intervention or to a wait list control. Intervention participants received a Jawbone Up24 monitor, a tablet with the Jawbone Up app installed, and brief weekly telephone counseling. Participants set daily and weekly step goals and used the monitor's idle alert to notify them when they were sedentary for more than 1 h. Interventionists provided brief counseling once per week by telephone. Feasibility was measured using observation and study records, and acceptability was measured by self-report using validated items. Physical activity and sedentary time were measured using ActivPAL monitors following standard protocols. Body composition was measured using dual-energy x-ray absorptiometry scans, and fitness was measured using a 6-min walk test. RESULTS:Participants were 61.48 years old (SD 5.60), 85% (34/40) female, 65% (26/40) white. Average activity monitor wear time was 81.85 (SD 3.73) of 90 days. Of the 20 Up24 monitors, 5 were reported broken and 1 lost. No related adverse events were reported. Acceptability items were rated at least 4 on a scale of 1-5. Effect sizes for most outcomes were small, including stepping time per day (d=0.35), steps per day (d=0.26), sitting time per day (d=0.21), body fat (d=0.17), and weight (d=0.33). CONCLUSIONS:The intervention was feasible and acceptable in this population. Effect sizes were similar to the sizes found using other wearable electronic activity monitors, indicating that when combined with telephone counseling, wearable activity monitors are a potentially effective tool for increasing physical activity and decreasing sedentary behavior. TRIAL REGISTRATION:Clinicaltrials.gov NCT01869348; https://clinicaltrials.gov/ct2/show/NCT01869348 (Archived by WebCite at http://www.webcitation.org/6odlIolqy).

Project description:BACKGROUND:To experience external objects in such a way that they are perceived as an integral part of one's own body is called embodiment. Wearable technology is a category of objects, which, due to its intrinsic properties (eg, close to the body, inviting frequent interaction, and access to personal information), is likely to be embodied. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities with regard to quantifying wearable technology embodiment are of particular value to the mobile health (mHealth) field. For example, the ability to predict the effectiveness of mHealth interventions and knowing the extent to which people embody the technology might be crucial for improving mHealth adherence. To facilitate examining wearable technology embodiment, we developed a measurement scale for this construct. OBJECTIVE:This study aimed to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well-known constructs related to technology adoption. The introduced instrument has 3 dimensions and includes 9 measurement items. The items are distributed evenly between the 3 dimensions, which include body extension, cognitive extension, and self-extension. METHODS:Data were collected through a vignette-based survey (n=182). Each respondent was given 3 different vignettes, describing a hypothetical situation using a different type of wearable technology (a smart phone, a smart wristband, or a smart watch) with the purpose of tracking daily activities. Scale dimensions and item reliability were tested for their validity and Goodness of Fit Index (GFI). RESULTS:Convergent validity of the 3 dimensions and their reliability were established as confirmatory factor analysis factor loadings (>0.70), average variance extracted values (>0.50), and minimum item to total correlations (>0.40) exceeded established threshold values. The reliability of the dimensions was also confirmed as Cronbach alpha and composite reliability exceeded 0.70. GFI testing confirmed that the 3 dimensions function as intercorrelated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (ie, trust, perceived usefulness, involvement, attitude, and continuous intention). CONCLUSIONS:The wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual's body, cognition, and self, as well as predict certain aspects of technology adoption. This 3-dimensional instrument can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users' personal identity. Further research is recommended to apply this measurement instrument to multiple scenarios and technologies, and more diverse user groups.

Dataset Information

Use of a Wearable Technology and Motivational Interviews to Improve Sleep in Older Adults With Osteoarthritis and Sleep Disturbance: A Pilot Study.

Publications

Use of a Wearable Technology and Motivational Interviews to Improve Sleep in Older Adults With Osteoarthritis and Sleep Disturbance: A Pilot Study.

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