The feasibility and acceptability of a web-based physical activity for the heart (PATH) intervention designed to reduce the risk of heart disease among inactive African Americans: Protocol for a pilot randomized controlled trial

Kariuki, Jacob K; Gibbs, Bethany Barone; Erickson, Kirk I.; Kriska, Andrea M.; Sereika, Susan M.; Ogutu, David; Milton, Heather; Wagner, La’Vette; Rao, Neel; Peralta, R. J.; Bobb, Jennifer F.; Bermudez, Adrian; Hirshfield, Sabina; Goetze, Timothy; Burke, Lora E.

doi:10.1016/j.cct.2021.106380

Cited by 4 publications

(17 citation statements)

References 38 publications

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“…The intervention systems collected user information in various ways; for example, using fitness trackers [ 41 , 44 , 50 - 52 , 59 , 61 ] (7/18, 39%), mobile phone sensors [ 50 , 54 , 56 - 59 ] (6/18, 33%), self-reported questionnaire [ 37 , 46 , 51 , 55 , 57 ] (5/18, 28%), smartwatches [ 35 , 53 , 54 , 57 , 59 ] (5/18, 28%), or through SMS text messages [ 44 , 49 , 60 ] (3/18, 17%). Of the 18 studies, 6 (33%) [ 44 , 50 , 51 , 54 , 57 , 59 ] had used more than one means to collect user information.…”

Section: Resultsmentioning

confidence: 99%

“…The target populations of the studies involved in this review were varied. They included older adults [ 53 , 55 , 57 ], children and parents [ 44 , 60 ], healthy adults who were sedentary [ 41 ], men and women with overweight [ 35 ], women with overweight who were sedentary [ 50 ], Hispanic individuals with overweight [ 61 ], African American individuals who were physically inactive [ 52 ], local residents [ 56 , 60 ], pregnant women [ 37 , 49 ], patients with cancer [ 51 , 54 ], and patients with chronic disease [ 46 , 58 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of the 18 studies included in this review, 10 (56%) used user preference as input to provide PIT [ 37 , 41 , 44 , 46 , 49 , 51 , 52 , 55 , 60 , 61 ]. Users could configure their preferred time of day to receive the intervention messages.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 17 studies, 8 (47%) [ 37 , 46 , 49 , 51 , 52 , 55 , 60 , 61 ] used manual techniques that relied on human mediation from the health care provider or the participant to generate the PIT. It should be noted that these systems relied on human mediation only to create the PIT, not to deliver it.…”

Section: Resultsmentioning

confidence: 99%

“…In this category, the systems recorded user preferences of intervention timing to provide PIT. Of the 8 studies, 6 (75%) [ 46 , 49 , 51 , 52 , 60 , 61 ] recorded user preferences for receiving the intervention message at registration time or during follow-up sessions, whereas for the remaining 2 (25%) studies, the user could configure the intervention timing by means of a smartphone app [ 55 ] or a website portal [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

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Personalization of Intervention Timing for Physical Activity: Scoping Review

Chaudhari¹,

Ghanvatkar²,

Kankanhalli³

2022

JMIR Mhealth Uhealth

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Background The use of sensors in smartphones, smartwatches, and wearable devices has facilitated the personalization of interventions to increase users’ physical activity (PA). Recent research has focused on evaluating the effects of personalized interventions in improving PA among users. However, it is critical to deliver the intervention at an appropriate time to each user to increase the likelihood of adoption of the intervention. Earlier review studies have not focused on the personalization of intervention timing for increasing PA. Objective This review aims to examine studies of information technology–based PA interventions with personalized intervention timing (PIT); identify inputs (eg, user location) used by the system for generating the PIT, the techniques and methods used for generating the PIT, the content of the PA intervention, and delivery mode of the intervention; and identify gaps in existing literature and suggest future research directions. Methods A scoping review was undertaken using PsycINFO, PubMed, Scopus, and Web of Science databases based on a structured search query. The main inclusion criteria were as follows: the study aimed to promote PA, included some form of PIT, and used some form of information technology for delivery of the intervention to the user. If deemed relevant, articles were included in this review after removing duplicates and examining the title, abstract, and full text of the shortlisted articles. Results The literature search resulted in 18 eligible studies. In this review, 72% (13/18) of the studies focused on increasing PA as the primary objective, whereas it was the secondary focus in the remaining studies. The inputs used to generate the PIT were categorized as user preference, activity level, schedule, location, and predicted patterns. On the basis of the intervention technique, studies were classified as manual, semiautomated, or automated. Of these, the automated interventions were either knowledge based (based on rules or guidelines) or data driven. Of the 18 studies, only 6 (33%) evaluated the effectiveness of the intervention and reported positive outcomes. Conclusions This work reviewed studies on PIT for PA interventions and identified several aspects of the interventions, that is, inputs, techniques, contents, and delivery mode. The reviewed studies evaluated PIT in conjunction with other personalization approaches such as activity recommendation, with no study evaluating the effectiveness of PIT alone. On the basis of the findings, several important directions for future research are also highlighted in this review.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Personalization of Intervention Timing for Physical Activity: Scoping Review

Chaudhari¹,

Ghanvatkar²,

Kankanhalli³

2022

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

A Decision Tree Analysis on the Impact of a Technology-Based Program on Symptom Distress

Chee

2021

CIN: Computers, Informatics, Nursing

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Using a decision tree analysis, this study aimed to identify the characteristics of the groups within Asian American breast cancer survivors whose symptom distress scores were effectively improved by a technology-based program. This was a secondary analysis of the data from an ongoing randomized controlled trial among 115 Asian American breast cancer survivors. The instruments were questions on background factors; the Memorial Symptom Assessment Scale-Short Form; the Cancer Behavior Inventory; the Questions on Attitudes, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention; and the Supportive Care Needs Survey-Short Form 34. The data were analyzed using chi-square tests, t tests, repeated measurement analyses, and decision tree analyses. The decrease in the global distress index scores was the largest (1.253 points) among those with high psychological support needs. The decrease in the physical symptom distress scale scores was the largest (1.133 points) among those with high physical and daily living support needs who had a short US residence period and who were young. The decrease in the psychological symptom distress scores was the largest (1.511) among those with high psychological support needs. The findings suggest several characteristics of the groups within Asian American breast cancer survivors whose symptom distress could be highly improved by a technology-based intervention.

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