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N. Bajdek1,2, N.K. Latham2, M. Dishaw1,2, S. Farrell1,2, Y.V. Shang2, K.M. Pencina2, R. Valderrábano2, M. McAlevey1, R. Dixon1, A. Williams3, N. Hachen3, K.F. Reid1,2,4


1. Laboratory of Exercise Physiology and Physical Performance, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 2. Research Program in Men’s Health: Aging and Metabolism, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Best Buy Health Inc., Boston, MA, USA; 4. Boston Claude D. Pepper Older Americans Independence Center for Function Promoting Therapies, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Corresponding Author: Kieran F. Reid, PhD, MPH, Laboratory of Exercise Physiology and Physical Performance, Boston Claude D. Pepper Older Americans Independence Center for Function Promoting Therapies, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA 02155, USA, Email: kfreid@bwh.harvard.edu

J Frailty Aging 2024;in press
Published online April 10, 2024, http://dx.doi.org/10.14283/jfa.2024.35



BACKGROUND: Falls are a leading cause of disability, institutionalization and mortality for older adults. More effective strategies to prevent falls are essential and may help at-risk older adults continue to live independently. While exercise programs with in-person supervision reduce fall risk, there are numerous barriers associated with older adults’ participation in such programs. Digitally delivered exercise interventions utilizing wearable technology may be an alternative fall prevention strategy for many vulnerable older adults. OBJECTIVES: To evaluate the feasibility of a scalable, multicomponent, remotely delivered, digital fall prevention exercise intervention for community-dwelling older adults with elevated fall risk. DESIGN: This single arm intervention trial enrolled older adults who reported ≥ 2 falls, or ≥ 1 injurious fall in the past year, or fear of falling.
STUDY SETTING AND PARTICIPANTS: Community-dwelling adults aged ≥ 65 years were recruited from the greater Boston region, MA, USA.
INTERVENTION: The 12-week multicomponent intervention was delivered via tablet and wearable sensors and consisted of a program of progressive moderate-intensity strength, power and balance training, adaptive aerobic walking exercise, regular coaching calls and digital motivational messaging.
MEASUREMENTS: Intervention adherence and measures of intervention feasibility, acceptability, and appropriateness were evaluated. Intervention effects on measures of fall risk, physical and cognitive performance, and other measures of well-being were also examined.
RESULTS: Twenty-three participants enrolled in the study and 20 completed the intervention (mean age: 76.3±5.5 yrs; BMI: 26.9±4.6 kg/m2; short physical performance battery score: 8.8 ± 2.2; 70% female). Overall adherence rates were 84.4±14.6% with no serious adverse events. Significant reductions in fear of falling and improvements in cognition and technology readiness were elicited (p≤0.04).
CONCLUSION: This study has demonstrated the feasibility of a multicomponent digital fall prevention exercise intervention for at-risk older adults. Additional studies are warranted to establish the efficacy of this highly scalable fall prevention strategy.

Key words: Falls, multicomponent intervention, exercise, digital.




Falls have serious consequences and are a leading cause of disability, institutionalization, and mortality for community-dwelling older adults (1, 2). One in four Americans aged 65 years and older experience a fall each year (3) which contributes to significant economic burden and medical costs (4). Given that the population of adults aged 65 years and older is projected to surpass 80 million by 2040 (5), more effective and scalable strategies are necessary to reduce fall risk and help older adults to continue to live independently.
Exercise training remains the most effective intervention to reduce fall risk and the occurrence of falls in community-dwelling older adults (6, 7). In particular, supervised exercise training that has incorporated muscle strengthening and balance training elements have thus far been shown to be one of the most promising approaches for reducing falls and fall risk in older adults (8-10). However, financial limitations, transportation restrictions, and lack of available resources can pose major barriers to participation in supervised exercise training, particularly for at-risk older adults (11, 12). While home-based fall prevention exercise strategies may be a more practical alternative, to date, many at-home approaches have been limited by adherence and intervention fidelity challenges, or they have often been delivered by healthcare providers such as physical therapists which majorly limits widespread scalability (6, 8, 10, 13).
Older adults have shown a growing interest in learning how to use and engage with new technology (14, 15) and the popularity of exercise programming for older adults and use of wearable technology continues to rise (16). Several recent efforts to digitally deliver fall prevention exercise interventions have been conducted. Despite major heterogeneity in the design, intervention content, adherence and fidelity monitoring, use of behavioral strategies, and fall risk status of the older adults studied, these digital intervention strategies hold promise as an effective intervention approach to reach and potentially mitigate fall risk in older adults (17-20).
The primary purpose of the current study was to examine the feasibility of a multicomponent digital fall prevention exercise intervention for at-risk, community-dwelling older adults where the exercise intervention and coaching would be delivered entirely remotely. In contrast to prior studies, we sought to develop a tablet-based intervention that incorporated several novel fall prevention strategies into the same intervention to synergistically address fall risk parameters in at-risk older adults. Key elements of our multicomponent intervention included a moderate-intensity muscle strength, power, and balance training regimen that emphasized functional, task-specific movements with progressive increases in muscle velocities. This type of exercise training may better target some of the underlying physiological impairments that precipitate falls in older adults. Other innovative aspects of our intervention included individualized and adaptive step goal targets designed to increase overall aerobic walking activity, and the enlistment of regular telephone-based motivational coaching support from an exercise physiologist in combination with regular tablet-based messaging to promote adherence to the intervention. Our primary hypothesis was that our multicomponent digital fall prevention exercise intervention would be feasible and acceptable for older adults who are at increased risk for falls. We also examined the preliminary effects of the intervention on fall risk parameters, measures of physical and cognitive performance, other self-reported measures of well-being, and new technology acceptance.



Study Design and Setting

This single-arm feasibility study was conducted at the Laboratory of Exercise Physiology and Physical Performance, Brigham and Women’s Hospital, Boston, MA, USA. All participants received the 12-week home-based multicomponent digital fall prevention exercise intervention which was remotely supported by an exercise physiologist. This study was conducted according to the guidelines of the Helsinki Declaration of 1975 and approved by the Institutional Review Board for Human Subjects Research of the Mass General Brigham healthcare system. This trial was registered at ClincialTrials.gov (NCT05432674).

Study Participants


Community-dwelling men and women aged 65 years and older who reported having two or more falls in the past year, or one or more injurious falls in the past year, or fear of falling were recruited from the greater Boston, MA region via online advertisements, clinician referrals, local senior centers, and participant word of mouth.

Screening Procedures and Eligibility

Interested individuals were pre-screened via telephone to confirm that they were able to walk independently with or without a walking aid, were fluent in English, lived independently (including living in their own home or independent senior housing), and were not enrolled in a formal exercise or physical therapy program (≥ 3 times per week). Individuals were excluded if they had significant vision or hearing impairment, planned orthopedic surgery, or pulmonary decompensation that would prevent or limit participation in the study. Those who had spinal surgery, fractures or joint replacements, chest pain or uncontrolled cardiovascular disease, myocardial infarction, acute coronary syndrome, revascularization surgery or stroke in the previous 6 months were also excluded from participation. Individuals who resided in nursing home facilities, planned to be without tablet access for >7 days during the intervention, or who were unable to attend in-person pre- or post-study clinic visits were also considered ineligible. Those that qualified for the in-person visit were invited to attend additional screening procedures at the laboratory.
Individuals provided information on their health and fall history and were asked to complete the Short Physical Performance Battery (SPPB) to assess lower extremity functional performance (23, 24). Participants who scored ≥ 5 on the SPPB, >17 on the Montreal Cognitive Assessment (MoCA) (25), and received medical clearance for participation by the study physician were considered eligible. Signed informed consent was obtained from all participants.

Multicomponent Digital Fall Prevention Exercise Intervention

We designed a novel 12-week multicomponent fall prevention exercise intervention that consisted of the following major elements in a single intervention: 1) digital delivery of intervention via exercise videos accessed through a tablet interface and wearable sensors; 2) a progressive, moderate-intensity, functional strength, power and balance exercise training regimen with embedded educational content; 3) an adaptive daily aerobic walking prescription; 4) regular motivational coaching telephone calls from a study interventionist; 5) regular tablet-based messaging to further promote intervention adherence (Figure 1). Participants were familiarized to the study tablet, wearable sensors, tablet accessories, adjustable ankle weights, and components of exercise intervention during the in-clinic assessment visit and before beginning the intervention. The participants completed all exercise sessions in their own home. After the initial in-person session to familiarize participants with the technology, no other exercise or motivational coaching sessions were conducted in person.

Figure 1. The 12-week multicomponent fall prevention exercise intervention included: 1) digitally delivered exercise training videos with embedded educational content via tablet device and wearable sensors; 2) progressive, moderate-intensity resistance and balance exercises; 3) an adaptive daily aerobic walking prescription; 4) regular motivational coaching telephone calls from an exercise expert; 5) regular tablet-based messages to provide encouragement


Tablet and Sensors

Best Buy Health, Inc. provided the proprietary digital system and mobile hotspots for this study. The intervention was delivered remotely via a tablet device that was pre-programmed with exercise videos for progressive resistance and balance training sessions, as well as videos for guidance on safely increasing walking activity and proper ankle weight use. Participants wore proprietary research-grade wrist- and lanyard-based sensors to monitor their physical activity and detect potential falls throughout their study participation. Sensor wear compliance was regularly monitored throughout the course of the study and participants were instructed to wear the sensors continuously during waking hours. If a participant wore the sensors for ≥14 hours a day, it was considered a sensor compliant day.

Functional Strength, Power, and Balance Exercises

The exercise intervention was adapted from prior studies and consisted of structured, moderate-intensity resistance training for muscle strength and power for the lower extremities, and a series of balance training exercises. Adjustable ankle weights were provided to each participant for the resistance training exercises. Examples of these exercises included chair stands, seated knee extensions, standing leg curls, and hip raises to build lower extremity strength. Examples of balance training exercises included one leg stands, tandem stands, and toe and heel walking that challenged participants’ center of gravity during static and dynamic movements. The intervention was divided into 3 x 4-week phases that progressively increased in training volume, duration, complexity, and speed of movement. Participants were instructed on how to report the modified category ratio rating of perceived exertion (RPE) scale (0-10) to gauge exercise intensity (28) with a target of 4-6 for resistance training and balance sessions. Progression was operationalized based on participant feedback, tablet-based post-exercise surveys, functional ability and review of progress by the study interventionist and study staff. Participants were required to perform the resistance and balance training 3 days per week on nonconsecutive days. The video durations were approximately 12, 14, and 18 minutes for each of the 3 phases of the program, respectively. Educational content that emphasized the potential physiological and functional benefits of the specific strength, power and balance exercises was embedded in all videos.

Adaptive Daily Walking Goals

Daily and weekly step goals were initially determined at baseline during a 7-day assessment of walking activity. Wearable sensors were distributed and collected in-person at baseline and follow-up visits, respectively, and the assessment began the day after the in-clinic assessment visit and was completed prior to beginning any intervention activities. Participants were instructed to go about their usual activity for 7 days while wearing the wrist and lanyard sensors continuously and that the sensors should only be removed for bathing and sleeping if desired. Valid wear time was defined as having a minimum of 10 compliant sensor worn hours in a single 24-hour day for a minimum of 5 days, which is consistent with best practices for objective measures of physical activity (29, 30). Participants were then encouraged to meet individualized daily steps goals that were based on their average walking activity during the baseline period.
Daily step goals were updated weekly according to participants’ adherence to meeting their daily or weekly goals over the previous week. Participants were instructed to wear the sensors continuously throughout their study participation. Goals were increased if participants met their daily step goal on 3 days of the week or if they met their weekly cumulative goal. Daily step goals were increased conservatively by 100 steps for low active (<3,000 steps per day) or moderately active participants (3,000-7,000 steps per day) and by 150 steps for participants who were highly active (>7,000 steps per day). If step goals were not met for 2 consecutive weeks, the average daily steps of those 2 weeks were used to re-set the goal to avoid de-motivating participants. Participants were able to view their daily step goal and their progress towards meeting that goal via the tablet device and they were encouraged to continue wearing the sensors during motivational coaching calls, which is consistent with established methods of wear compliance (29). Educational content on how to safely and effectively increase walking activity around participants’ home and community environments were included on the tablet. Participants were encouraged to begin their walking program slowly and to be aware of built environment considerations (e.g., sidewalk conditions) that may pose potential hazards, wear proper shoes and attire, and be properly hydrated.

Motivational Coaching Calls

The behavioral strategies and principles applied in this intervention were based upon a social cognitive model of acquisition and maintenance of health behavior (31). Motivational coaching calls were delivered via telephone by the interventionist (exercise physiologist) and included a brief 30-minute introductory call to review participants’ exercise history and to establish program goals. Weekly motivational coaching calls included social support (32) and problem solving (33) facets of behavior change to provide guidance and an opportunity reflect on performance over the previous week and information about each exercise session was recorded in real time using a digital call log. Motivational coaching calls were scheduled weekly during Weeks 1-4 and then biweekly throughout the remaining weeks of the intervention to provide motivation to continue in the intervention and discuss exercise progress and to ascertain any changes in health status. Additional motivational coaching calls were made to participants on an as-needed basis depending on the level of support deemed necessary by the study team. Outside of motivational coaching calls, participants were encouraged to self-monitor their progress using feedback provided on the tablet device and exercise logs. In addition to the motivational coaching calls, participants received regular pre-programmed tablet-based messages that included performance-based encouragement as well as routine reminder notifications if exercises had not been completed in the last 3 days or if the wearable sensors required charging. This motivational approach aligns with the affect-integration-motivation framework by providing a prompt, such as the motivational coaching call or the pre-programmed messages, with the participant then motivated to approach the task immediately or later in the day (34).

Evaluation of Feasibility, Acceptability and Safety

Feasibility and acceptability were assessed via the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), Feasibility of Intervention Measure (FIM) (35), and the Technology Acceptance Model (TAM) (36) during follow-up. Feasibility and acceptability were used to evaluate adherence to the intervention and participation in completing resistance and balance training videos each week. Adherence was reported as the percentage of exercise sessions completed over total possible exercise sessions until follow-up visit. Adherence rates were also adjusted to account for missed exercise sessions due to a medical illness or health event. Reasons for not completing a session were recorded by the study interventionist. Adverse events (AEs), including injuries that were incurred during exercise training, were monitored by the interventionist and evaluated for relatedness and seriousness by the study clinician. AEs were considered serious if they resulted in death, hospitalization, or a persistent or significant disruption of normal life functions. Safety assessment included a review of all AEs and SAEs by the study clinician, including injuries that were incurred during exercise training.

Fall Monitoring

Falls were tracked using the proprietary wearable sensors and tablet device as well as self-report. If a fall was detected, participants were prompted to answer tablet-based questions about how the fall occurred including time, date, and reason for the fall. Participants manually recorded fall information on the tablet if a fall occurred while they were not wearing the sensor, or if the wearable senors did not detect a fall. All fall information was reported to and reconciled by the study interventionist.

Additional Study Assessments

Physical activity level was assessed using the Physical Activity Scale for the Elderly (PASE) (37). Grip strength was measured in a seated position using an adjustable hydraulic hand dynamometer (JAMAR 211591, Sammons Preston, Bolingbrook, IL, USA) by having participants sit with their elbow resting on a table and squeezing the dynamometer as hard as possible twice with a 10-second rest in between. Peak force was recorded to the nearest tenth of a kilogram (38). The SPPB test and MoCA were repeated at the 12-week follow-up assessment.
The Digit Symbol Substitution Test (DSST) was used to assess cognitive performance and consisted of a 9-item digit symbol code key table and response table (39). Falls Self-Efficacy Scale (FES-I) was used to measure confidence in performing a range of activities of daily living without falling (40). Quality of Well-Being Scale Self-Administered (QWB-SA) was used to assess health problems participants may have experienced over the previous 3 days (41) and the Center for Epidemiologic Studies Depression Scale (CES-D) was used to assess depressive symptoms (42). The 5 components of the Fried Frailty Index were ascertained using data from the aforementioned questionnaires and study assessments (healthy history questionnaire [weight loss], CES-D [exhaustion] PASE [low physical activity] SPPB gait test [slowness], grip strength [weakness]) (43). The Technology Readiness Index (TRI 2.0) is a 36-item scale that was used to assess individuals’ willingness to embrace and use technology (44). The assessments of physical function, cognitive performance and study questionnaires were administered by the same study assessor at baseline and the 12-week follow-up assessment.

Statistical Analysis

Descriptive statistics were used to summarize the measures of feasibility and intervention adherence. Mean, standard deviation, minimum and maximum were provided for continuous variables and number and percentages for categorical data. For the study assessments, we prespecified an adherers analysis based on an intervention adherence rate of ≥ 70%. The one-sample paired T-Test and the McNemar’s test were used to assess difference between baseline and follow-up assessments. Two-sided type I error alpha level was set at 0.05 for all hypotheses testing. Statistical analyses were performed using IBM SPSS Statistics 26 software (SPSS, Inc. Chicago, IL, USA) or SAS Version 9.3 or higher (SAS Institute, Inc. Cary, NC).



Recruitment and Baseline Participant Characteristics

Participant recruitment was initiated in June 2022 and continued through February 2023. A total of 52 individuals responded to recruitment materials. Online advertisements yielded the highest proportion of respondents (53.8%) followed by clinician referrals (23.1%). Other recruitment sources were word of mouth (13.5%), referrals from a local suburban senior center (7.7%), and clinicaltrials.gov (1.9%). Of these respondents, 46 were pre-screened to determine preliminary eligibility for in-person screening and baseline assessments. Twenty-four participants attended the screening visit and 1 was excluded from the study (MoCA score <17). One other participant was unable to fully complete the baseline assessments due to personal reasons and was withdrawn by the study investigators. Of the 22 participants who completed baseline assessments and started the intervention, 2 participants withdrew for personal reasons during Weeks 4 and 5. Twenty participants completed the intervention and follow-up assessments.
Baseline descriptive statistics are presented in Table 1 for participants who completed the intervention. Most participants were female, well-educated and had several chronic medical conditions. The vast majority of participants (95%) reported a fear of falling and over one-third of the participants (35%) had experienced 2 or more falls within the past year or injurious fall within the past year. On average the study sample had mild to moderate mobility-limitations as determined by the SPPB, with 30% of participants having severe mobility impairments (SPPB score ≤7). Nine (45%) and 2 (10%) participants were pre-frail or frail, respectively. Two participants (10%) resided in independent senior housing with the remainder (90%) free-living in the community. Eight participants (40%) reported living alone while 12 (60%) reported living in their home with at least one other person.

Table 1. Baseline Participant Characteristics (n=20)

Values are presented as n (%) or mean ± SD for participants who completed the intervention. n: number of participants; kg: kilograms; BMI: body mass index; kg/m2: body weight in kilo-grams over height in meters squared; SPPB: Short Physical Performance Battery; m/s: gait speed in meters per second.


Intervention Safety

No SAEs and no hospitalizations occurred during this study. Four participants experienced non-serious AEs related or probably to the intervention (rash from the wearable wrist sensor, chronic knee pain due to treadmill walking, fall while walking over an uneven surface). Four participants experienced 5 AEs that were not related to study activities.


Intervention Feasibility, Acceptability, and Adherence

Summary scores for AIM, IAM, and FIM at follow-up are presented in Figure 2A for all participants. The highest possible score is 20 with higher scores indicating greater intervention acceptability, appropriateness, and feasibility. Additional intervention metrics for all participants are presented in Figure 2B-D. Adherence was maintained at approximately 80% for all participants throughout the intervention duration (Figure 2B). Self-reported adherence rates corresponded to 81.5 ± 18.2% (median 85.4%) with mean rates of 83.6 ± 18.4, 85.5 ± 15, and 75.1 ± 29% for Weeks 1-4, Weeks 5-8, and Weeks 9-12, respectively. When adherence was adjusted to account for medical illnesses/health events, it corresponded to 84.4 ± 14.6% (median 87.5%). The adjusted adherence rates for each phase of the intervention were 86.4 ± 15.6, 87.8 ± 14.4, and 77.2 ± 29.4%, respectively. Compliance with wearing the sensor corresponded to 94.0%. A total of 16 of the 20 participants (80%) who completed the intervention had an adherence rate ≥ 70%.

Figure 2. Interventon Feasibility, Acceptability and Fidelity (n = 20)

A) Values for Acceptability of Intervention (AIM), Intervention Appropriateness (IAM) and Feasibility of Intervention (FIM); B) Exercise adherence rates thoughout each phase of intervention; C) Progressive increase of ankle weight resistance during intervention; D) Rate of preceived exertion (RPE) values from participants throughout intervention (target RPE in red text box). Values are mean ± SD.


Ankle weight resistance increased over the course of the intervention (Figure 2C) and RPE was maintained at approximately the prescribed intensity (4-6) (Figure 2D), indicating both optimal intervention fidelity and progression. Participants received an average of 8.8 ± 0.5 motivational coaching telephone calls from the study interventionist over the course of the intervention with calls lasting 11.8 ± 1.9 minutes on average.
Baseline and follow-up scores for the SPPB and daily step counts are presented in Figure 3A-C. For the subset of participants who had an adherence rate ≥ 70%, significant improvements were observed in the total SPPB score (mean difference: 1.94 ± 2.4, p=0.004) and the balance (mean difference: 0.69 ± 1.2, p=0.03) and chair stand (mean difference: 1.13 ± 1.1, p=0.001) subcomponent scores. Average daily step counts were increased by approximately 20% at follow-up compared to baseline.

Figure 3. Improvements in SPPB scores and daily step counts among participants with ≥ 70% intervention adherence (n=16)

A) Total SPPB score (* = P ≤ 0.004); B) SPPB subcomponent scores (* = P ≤ 0.03); C) Average dailty step counts.


Fear of falling, Falls and Other Study Assessments

Table 2 details the impact of the intervention on fear of falling, occurrence of falls, and other measures of physical and cognitive performance, perceived well-being, technology acceptance and technology readiness. We observed significant reductions in the fear of falling (p=0.03) and a trend for reduction in occurrence of falling (p=0.03) following the intervention. There was a significant improvement on MoCA performance (mean difference: 1.2 ± 2.2, p=0.05) and technology readiness (mean difference: 5.0 ± 6.7, p=0.01). There were no changes in body weight or handgrip from baseline to follow-up. Due to the small sample sizes, we were likely underpowered to detect statistically significant pre- to post-intervention differences for many of these outcome measures, hence these data should be interpreted cautiously.

Table 2. Impact of Intervention on Fear of Falling, Occurrence of Falls, and Other Study Assessments (n=16)

Values are presented as n (%) or mean±SD. n: number of participants; † Falls in the past year; Fallers: Number of participants who experienced at least 1 fall; MoCA: Montreal Cognitive Assessment; DSST: Digit Symbol Substitution Test; CES-D: Center for Epidemiologic Studies Depression Scale; QWB-SA: Quality of Well-Being Scale Self-Administered; kg, kilograms, PASE: Physical Activity Scale for The Elderly



This investigation has demonstrated the feasibility and acceptability of a multicomponent digital fall prevention exercise intervention delivered entirely remotely to at-risk older adults with increased fall risk. We observed excellent adherence rates, minimal safety concerns as well as significant improvements in meaningful endpoints of importance to older adults, such as lower extremity physical performance, balance, fear of falling, cognition, and technology readiness. We also observed notable trends for increases in walking activity and trends for reductions of falls among strong adherers to the intervention.
Comparable feasibility and acceptability findings have been reported through evaluations of adherence and qualitative interviews in previous investigations that deployed digitally delivered fall prevention exercise programs (14, 19), however, we observed higher adherence and lower participant attrition compared to the adherence rates (63-73%) and attrition (15-28%) of similar digital programs (6, 45, 46). We speculate that our excellent adherence rates are largely due to the multicomponent nature of our intervention, of which included an easy to use and engaging digital platform, provision of educational content on the physiological benefits of the different types of functional exercises, and in particular the regular motivational coaching calls and direct remote support from an exercise physiologist. While previous investigations have included self-guided behavior change support via educational handouts, goal setting, and activity planning (17, 18), individualized motivational support from an exercise expert has not been consistently implemented in prior digital fall prevention programs. In addition, our inclusion of educational content, which emphasized the potential benefits and physiological impact of the various exercises on daily activities of living, may have had a notable motivational impact on participants. Educational content has also been used successfully in previous digital or exergaming interventions (18-20, 45, 47). In line with other prior studies (17, 19, 45), the regular digital reminders sent to the tablet device, combined with regular feedback and guidance from the trained interventionist, may have provided the necessary support to overcome any technology barriers that may otherwise deter older adults from participating in digital exercise programs.
Previous digital or exergaming investigations have primarily included traditional lower extremity muscle strengthening, balance, and gait exercises often performed for 30-45 minutes on 2-3 days per week as demonstrated with Safe Step (17, 19) and Otago-based programs (45, 47). In the current study, we utilized shorter duration exercise videos which can be considered more time efficient when compared to the longer video duration of other digital programs (17-19). Our exercise prescription also particularly emphasized functional, task specific movements with regular monitoring of dose and intensity to appropriately progress participants in their exercise program and to ensure an appropriate level of challenge, which has not routinely been implemented or reported in other digital offerings (18). This regular monitoring of intervention adherence and fidelity by an exercise expert may be critical for the successful implementation of digital interventions and for inducing the necessary physiological adaptations (7).
We also observed a meaningful increase in walking activity among participants who adhered to the intervention, which is a noteworthy finding given the at-risk frail population recruited for this study. The average step counts of our study sample is comparable with step counts (~4,000 steps/day) observed amongst community-based older adults, many of whom had a variety of chronic medical conditions (48). Our observed increase in step count activity was higher than what has been considered meaningful improvements in step counts among community-based older adults who engage in physical activity interventions (48). Adaptive walking prescriptions have not been consistently included or emphasized in digitally delivered fall prevention exercise programs for at-risk older adults despite the positive benefits of increased physical activity on reducing incidence of major mobility-disability (26) and fall occurrence (19). We observed changes in several measures including lower extremity physical performance and cognition, which are of magnitudes widely accepted as meaningful, patient important improvements (49, 50).
Our study sample was well characterized and representative of older adults at high risk of falls which is in contrast to previous investigations with less frail study samples (17, 45) or those with participants who did not have a recent fall history (19). The majority of participants also had several chronic medical conditions and almost half of participants lived alone which reaffirms the potential of digital interventions to reach and motivate at-risk older adults to exercise in their own homes (6, 17, 45). Our study has also demonstrated preliminary feasibility of completing clinic-based assessments with at-risk older adults who often encounter transportation and accessibility challenges to attending center-based assessments and programs. Given the remote delivery of many digital programs, previous investigations have relied upon virtual assessments (17) or self-reported outcome metrics (19) to assess fall risk and occurrence, therefore, the true impact of these interventions on clinical measures of fall risk and objective measures of physical performance following a home-based digital program may have been preliminary (45) or unexamined (14, 19, 20).


There were several potential limitations of the present study. First, the data should be interpreted with caution due to the small sample size and lack of a control group. However, our preliminary findings add new information to the literature on remotely delivered digital fall prevention exercise interventions for at-risk older adults. Similar to prior studies, our sample lacked racial, ethnic, gender, and educational diversity, although the characteristics of our study population were representative of older adults at high risk of falls. Some participants reported having technical difficulty with operating the tablet, however, the regular motivational coaching calls allowed the opportunity for the interventionist to individually troubleshoot technology challenges as needed. Unlike previous center-based programs that have posed transportation and accessibility barriers, our remote system allowed participants to exercise independently in their homes according to their own schedule. Lastly, study staff were unblinded given the lack of a control group.



This study has successfully demonstrated that a multicomponent digital fall prevention exercise intervention is feasible and acceptable for at-risk older adults. We also reported improvements in several meaningful endpoints of particular importance to the daily functioning of vulnerable older adults. Larger randomized controlled trials are warranted to appropriately establish the efficacy of our multicomponent strategy which, if successful, could have substantial benefits for large populations of at-risk older adults.


Ethics approval and consent to participate: This study was approved by the Mass General Brigham Institutional Review Board. A written informed consent was obtained from all participants.

Funding: This study was supported by Best Buy Health, Inc. and the Boston Claude D. Pepper Older Americans Independence Center (1P30AG031679). The sponsor (Best Buy Health, Inc.) had a role in design and conduct of the study, and in the review and approval of the manuscript.

Author Contributions: Initial drafting of manuscript: NB and KFR. Critical revision of the manuscript and final approval for submission: all authors. All authors have read and agreed to the published version of the manuscript.

Acknowledgements: The study authors wish to acknowledge Joseph T. Gwin, Kelly Urbany, Eric S. Kirkendall, Jantira T. Thomas and Jason Fanning for their contributions to initial concept development and protocol design. We also wish to acknowledge Iman Khaghani-Far, Sarah-Paul McCarty, Ibukun Oduniyi, and Arlin Grijalba for their contributions to technology development and technology support throughout the implementation of the study.

Conflicts of Interest: AW and NH are employees of Best Buy Health, Inc., Boston, MA, USA. NKL and KFR received grant support (through institution) from Best Buy Health, Inc. to support this clinical trial.



1. Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. N Engl J Med. 1997;337(18):1279-1284. DOI: 10.1056/NEJM199710303371806
2. Alamgir H, Muazzam S, Nasrullah M. Unintentional falls mortality among elderly in the United States: time for action. Injury. 2012;43(12):2065-2071. DOI: 10.1016/j.injury.2011.12.001
3. Prevention. CfDCa. Older Adult Falls Reported by State. 2023; https://www.cdc.gov/falls/data/falls-by-state.html. Accessed September 01, 2023.
4. Florence CS, Bergen G, Atherly A, Burns E, Stevens J, Drake C. Medical Costs of Fatal and Nonfatal Falls in Older Adults. J Am Geriatr Soc. 2018;66(4):693-698. DOI: 10.1111/jgs.15304
5. (ACL). AfCL. Projected Future Growth of Older Population. 2022; https://acl.gov/aging-and-disability-in-america/data-and-research/projected-future-growth-older-population Accessed May 02, 2023.
6. Liu-Ambrose T, Davis JC, Best JR, et al. Effect of a Home-Based Exercise Program on Subsequent Falls Among Community-Dwelling High-Risk Older Adults After a Fall: A Randomized Clinical Trial. Jama. 2019;321(21):2092-2100. DOI: 10.1001/jama.2019.5795
7. Sherrington C, Michaleff ZA, Fairhall N, et al. Exercise to prevent falls in older adults: an updated systematic review and meta-analysis. Br J Sports Med. 2017;51(24):1750-1758. DOI: 10.1136/bjsports-2016-096547
8. Campbell AJ, Robertson MC, Gardner MM, Norton RN, Tilyard MW, Buchner DM. Randomised controlled trial of a general practice programme of home based exercise to prevent falls in elderly women. Bmj. 1997;315(7115):1065-1069. DOI: 10.1136/bmj.315.7115.1065
9. Chittrakul J, Siviroj P, Sungkarat S, Sapbamrer R. Multi-System Physical Exercise Intervention for Fall Prevention and Quality of Life in Pre-Frail Older Adults: A Randomized Controlled Trial. Int J Environ Res Public Health. 2020;17(9). DOI: 10.3390/ijerph17093102
10. Faber MJ, Bosscher RJ, Chin APMJ, van Wieringen PC. Effects of exercise programs on falls and mobility in frail and pre-frail older adults: A multicenter randomized controlled trial. Arch Phys Med Rehabil. 2006;87(7):885-896. DOI: 10.1016/j.apmr.2006.04.005
11. Merom D, Pye V, Macniven R, et al. Prevalence and correlates of participation in fall prevention exercise/physical activity by older adults. Prev Med. 2012;55(6):613-617. DOI: 10.1016/j.ypmed.2012.10.001
12. Sherrington C, Fairhall NJ, Wallbank GK, et al. Exercise for preventing falls in older people living in the community. Cochrane Database Syst Rev. 2019;1(1):Cd012424. DOI: 10.1002/14651858.CD012424.pub2
13. Mittaz Hager AG, Mathieu N, Lenoble-Hoskovec C, Swanenburg J, de Bie R, Hilfiker R. Effects of three home-based exercise programmes regarding falls, quality of life and exercise-adherence in older adults at risk of falling: protocol for a randomized controlled trial. BMC Geriatr. 2019;19(1):13. DOI: 10.1186/s12877-018-1021-y
14. Ambrens M, Stanners M, Valenzuela T, et al. Exploring Older Adults’ Experiences of a Home-Based, Technology-Driven Balance Training Exercise Program Designed to Reduce Fall Risk: A Qualitative Research Study Within a Randomized Controlled Trial. J Geriatr Phys Ther. 2023;46(2):139-148. DOI: 10.1519/JPT.0000000000000321
15. Pettersson B, Wiklund M, Janols R, et al. ‘Managing pieces of a personal puzzle’ – Older people’s experiences of self-management falls prevention exercise guided by a digital program or a booklet. BMC Geriatr. 2019;19(1):43. DOI: 10.1186/s12877-019-1063-9
16. Thompson W. Worldwide Survey of Fitness Trends for 2022. ACSM’S Health & Fitness Journal. 2022;26:11-20. DOI: 10.1249/FIT.0000000000000732
17. Bajraktari S, Zingmark M, Pettersson B, Rosendahl E, Lundin-Olsson L, Sandlund M. Reaching Older People With a Digital Fall Prevention Intervention in a Swedish Municipality Context-an Observational Study. Front Public Health. 2022;10:857652. DOI: 10.3389/fpubh.2022.857652
18. Delbaere K, Valenzuela T, Woodbury A, et al. Evaluating the effectiveness of a home-based exercise programme delivered through a tablet computer for preventing falls in older community-dwelling people over 2 years: study protocol for the Standing Tall randomised controlled trial. BMJ Open. 2015;5(10):e009173. DOI: 10.1136/bmjopen-2015-009173
19. Jacobson CL, Foster LC, Arul H, Rees A, Stafford RS. A Digital Health Fall Prevention Program for Older Adults: Feasibility Study. JMIR Form Res. 2021;5(12):e30558. DOI: 10.2196/30558
20. Pettersson B, Janols R, Wiklund M, Lundin-Olsson L, Sandlund M. Older Adults’ Experiences of Behavior Change Support in a Digital Fall Prevention Exercise Program: Qualitative Study Framed by the Self-determination Theory. J Med Internet Res. 2021;23(7):e26235. doi: 10.2196/26235
21. Reid KF, Fielding RA. Skeletal muscle power: a critical determinant of physical functioning in older adults. Exerc Sport Sci Rev. 2012;40(1):4-12. doi: 10.1097/JES.0b013e31823b5f13
22. Reid KF, Storer TW, Bhasin S. Functional exercise training plus promyogenic therapy: A winning formula for preventing and treating mobility-disability? J Am Geriatr Soc. 2023;71(6):2017-2022. DOI: 10.1111/jgs.18293
23. Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85-94. DOI: 10.1093/geronj/49.2.m85
24. Welch SA, Ward RE, Beauchamp MK, Leveille SG, Travison T, Bean JF. The Short Physical Performance Battery (SPPB): A Quick and Useful Tool for Fall Risk Stratification Among Older Primary Care Patients. J Am Med Dir Assoc. 2021;22(8):1646-1651. DOI: 10.1016/j.jamda.2020.09.038
25. de Ruiter SC, de Jonghe JFM, Germans T, Ruiter JH, Jansen R. Cognitive Impairment Is Very Common in Elderly Patients With Syncope and Unexplained Falls. J Am Med Dir Assoc. 2017;18(5):409-413. DOI: 10.1016/j.jamda.2016.11.012
26. Pahor M, Guralnik JM, Ambrosius WT, et al. Effect of structured physical activity on prevention of major mobility disability in older adults: the LIFE study randomized clinical trial. Jama. 2014;311(23):2387-2396. DOI: 10.1001/jama.2014.5616
27. Reid KF, Laussen J, Bhatia K, et al. Translating the Lifestyle Interventions and Independence for Elders Clinical Trial to Older Adults in a Real-World Community-Based Setting. J Gerontol A Biol Sci Med Sci. 2019;74(6):924-928. DOI: 10.1093/gerona/gly152
28. Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109-115. DOI: 10.1519/00124278-200102000-00019
29. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11 Suppl):S531-543. DOI: 10.1249/01.mss.0000185657.86065.98
30. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc. 2005;37(11 Suppl):S582-588. DOI: 10.1249/01.mss.0000185292.71933.91
31. Rejeski WJ, Axtell R, Fielding R, et al. Promoting physical activity for elders with compromised function: the lifestyle interventions and independence for elders (LIFE) study physical activity intervention. Clin Interv Aging. 2013;8:1119-1131. doi: 10.2147/CIA.S49737
32. Estabrooks PA, Munroe KJ, Fox EH, et al. Leadership in physical activity groups for older adults: a qualitative analysis. J Aging Phys Act. 2004;12(3):232-245. DOI: 10.1123/japa.12.3.232
33. Blair SN DA, Marcus BH, Carpenter RA, Jaret P. . Active Living Every Day. . 2nd ed. Champaign (IL): Human Kinetics; 2011.
34. Nahum-Shani I, Shaw SD, Carpenter SM, Murphy SA, Yoon C. Engagement in digital interventions. Am Psychol. 2022;77(7):836-852. DOI: 10.1037/amp0000983
35. Weiner BJ, Lewis CC, Stanick C, et al. Psychometric assessment of three newly developed implementation outcome measures. Implementation Science. 2017;12(1):108. DOI: 10.1186/s13012-017-0635-3
36. Davis F, Davis F. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 1989;13:319. DOI: 10.2307/249008
37. Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46(2):153-162.
38. Rantanen T, Guralnik JM, Foley D, et al. Midlife hand grip strength as a predictor of old age disability. Jama. 1999;281(6):558-560. DOI: 10.1001/jama.281.6.558
39. Stephens R, Kaufman A. The role of long-term memory in digit-symbol test performance in young and older adults. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2009;16(2):219-240. DOI: 10.1080/13825580802573060
40. Yardley L, Beyer N, Hauer K, Kempen G, Piot-Ziegler C, Todd C. Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age Ageing. 2005;34(6):614-619. DOI: 10.1093/ageing/afi196
41. Andresen EM, Rothenberg BM, Kaplan RM. Performance of a self-administered mailed version of the Quality of Well-Being (QWB-SA) questionnaire among older adults. Med Care. 1998;36(9):1349-1360. DOI: 10.1097/00005650-199809000-00007
42. Carleton RN, Thibodeau MA, Teale MJ, et al. The center for epidemiologic studies depression scale: a review with a theoretical and empirical examination of item content and factor structure. PLoS One. 2013;8(3):e58067. DOI: 10.1371/journal.pone.0058067
43. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-156. DOI: 10.1093/gerona/56.3.m146
44. Parasuraman AP, Colby C. An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research. 2014;18:59-74. DOI: 10.1177/1094670514539730
45. Mansson L, Lundin-Olsson L, Skelton DA, et al. Older adults’ preferences for, adherence to and experiences of two self-management falls prevention home exercise programmes: a comparison between a digital programme and a paper booklet. BMC Geriatr. 2020;20(1):209.
46. Silveira P, van de Langenberg R, van het Reve E, Daniel F, Casati F, de Bruin ED. Tablet-Based Strength-Balance Training to Motivate and Improve Adherence to Exercise in Independently Living Older People: A Phase II Preclinical Exploratory Trial. J Med Internet Res. 2013;15(8):e159. DOI: 10.2196/jmir.2579
47. Marston HR, Woodbury A, Gschwind YJ, et al. The design of a purpose-built exergame for fall prediction and prevention for older people. Eur Rev Aging Phys Act. 2015;12:13. DOI: 10.1186/s11556-015-0157-4
48. Tudor-Locke C, Craig CL, Aoyagi Y, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011;8:80. DOI: 10.1186/1479-5868-8-80
49. Krishnan K, Rossetti H, Hynan LS, et al. Changes in Montreal Cognitive Assessment Scores Over Time. Assessment. 2017;24(6):772-777. DOI: 10.1177/1073191116654217
50. Perera S, Mody SH, Woodman RC, Studenski SA. Meaningful change and responsiveness in common physical performance measures in older adults. J Am Geriatr Soc. 2006;54(5):743-749. DOI: 10.1111/j.1532-5415.2006.00701.x

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